forked from scikit-learn/scikit-learn.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathclasses.html
2905 lines (2872 loc) · 271 KB
/
classes.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>API Reference — scikit-learn 0.20.dev0 documentation</title>
<!-- htmltitle is before nature.css - we use this hack to load bootstrap first -->
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link rel="stylesheet" href="../_static/css/bootstrap.min.css" media="screen" />
<link rel="stylesheet" href="../_static/css/bootstrap-responsive.css"/>
<link rel="stylesheet" href="../_static/nature.css" type="text/css" />
<link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../_static/gallery.css" type="text/css" />
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
VERSION: '0.20.dev0',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt'
};
</script>
<script type="text/javascript" src="../_static/jquery.js"></script>
<script type="text/javascript" src="../_static/underscore.js"></script>
<script type="text/javascript" src="../_static/doctools.js"></script>
<script type="text/javascript" src="../_static/js/copybutton.js"></script>
<script type="text/javascript" src="../_static/js/extra.js"></script>
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_SVG"></script>
<link rel="shortcut icon" href="../_static/favicon.ico"/>
<link rel="author" title="About these documents" href="../about.html" />
<link rel="search" title="Search" href="../search.html" />
<link rel="next" title="sklearn.base.BaseEstimator" href="generated/sklearn.base.BaseEstimator.html" />
<link rel="prev" title="Understanding the decision tree structure" href="../auto_examples/tree/plot_unveil_tree_structure.html" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script src="../_static/js/bootstrap.min.js" type="text/javascript"></script>
<script>
VERSION_SUBDIR = (function(groups) {
return groups ? groups[1] : null;
})(location.href.match(/^https?:\/\/scikit-learn.org\/([^\/]+)/));
</script>
<link rel="canonical" href="http://scikit-learn.org/stable/modules/classes.html" />
<script type="text/javascript">
$("div.buttonNext, div.buttonPrevious").hover(
function () {
$(this).css('background-color', '#FF9C34');
},
function () {
$(this).css('background-color', '#A7D6E2');
}
);
function showMenu() {
var topNav = document.getElementById("scikit-navbar");
if (topNav.className === "navbar") {
topNav.className += " responsive";
} else {
topNav.className = "navbar";
}
};
</script>
</head>
<body>
<div class="header-wrapper">
<div class="header">
<p class="logo"><a href="../index.html">
<img src="../_static/scikit-learn-logo-small.png" alt="Logo"/>
</a>
</p><div class="navbar" id="scikit-navbar">
<ul>
<li><a href="../index.html">Home</a></li>
<li><a href="../install.html">Installation</a></li>
<li class="btn-li"><div class="btn-group">
<a href="../documentation.html">Documentation</a>
<a class="btn dropdown-toggle" data-toggle="dropdown">
<span class="caret"></span>
</a>
<ul class="dropdown-menu">
<li class="link-title">Scikit-learn <script>document.write(DOCUMENTATION_OPTIONS.VERSION + (VERSION_SUBDIR ? " (" + VERSION_SUBDIR + ")" : ""));</script></li>
<li><a href="../tutorial/index.html">Tutorials</a></li>
<li><a href="../user_guide.html">User guide</a></li>
<li><a href="#">API</a></li>
<li><a href="../glossary.html">Glossary</a></li>
<li><a href="../faq.html">FAQ</a></li>
<li><a href="../developers/contributing.html">Contributing</a></li>
<li class="divider"></li>
<script>if (VERSION_SUBDIR != "stable") document.write('<li><a href="http://scikit-learn.org/stable/documentation.html">Stable version</a></li>')</script>
<script>if (VERSION_SUBDIR != "dev") document.write('<li><a href="http://scikit-learn.org/dev/documentation.html">Development version</a></li>')</script>
<li><a href="http://scikit-learn.org/dev/versions.html">All available versions</a></li>
<li><a href="../_downloads/scikit-learn-docs.pdf">PDF documentation</a></li>
</ul>
</div>
</li>
<li><a href="../auto_examples/index.html">Examples</a></li>
</ul>
<a href="javascript:void(0);" onclick="showMenu()">
<div class="nav-icon">
<div class="hamburger-line"></div>
<div class="hamburger-line"></div>
<div class="hamburger-line"></div>
</div>
</a>
<div class="search_form">
<div class="gcse-search" id="cse" style="width: 100%;"></div>
</div>
</div> <!-- end navbar --></div>
</div>
<!-- GitHub "fork me" ribbon -->
<a href="https://github.com/scikit-learn/scikit-learn">
<img class="fork-me"
style="position: absolute; top: 0; right: 0; border: 0;"
src="../_static/img/forkme.png"
alt="Fork me on GitHub" />
</a>
<div class="content-wrapper">
<div class="sphinxsidebar">
<div class="sphinxsidebarwrapper">
<div class="rel rellarge">
<div class="rellink">
<a href="../auto_examples/tree/plot_unveil_tree_structure.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
Understanding...
</span>
<span class="hiddenrellink">
Understanding the decision tree structure
</span>
</a>
</div>
<div class="spacer">
</div>
<div class="rellink">
<a href="generated/sklearn.base.BaseEstimator.html"
accesskey="N">Next
<br/>
<span class="smallrellink">
sklearn.base....
</span>
<span class="hiddenrellink">
sklearn.base.BaseEstimator
</span>
</a>
</div>
<!-- Ad a link to the 'up' page -->
</div>
<p class="doc-version"><b>scikit-learn v0.20.dev0</b><br/>
<a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a></p>
<p class="citing">Please <b><a href="../about.html#citing-scikit-learn" style="font-size: 110%;">cite us </a></b>if you use the software.</p>
<ul>
<li><a class="reference internal" href="#">API Reference</a><ul>
<li><a class="reference internal" href="#module-sklearn.base"><code class="docutils literal"><span class="pre">sklearn.base</span></code>: Base classes and utility functions</a><ul>
<li><a class="reference internal" href="#base-classes">Base classes</a></li>
<li><a class="reference internal" href="#functions">Functions</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.calibration"><code class="docutils literal"><span class="pre">sklearn.calibration</span></code>: Probability Calibration</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.cluster"><code class="docutils literal"><span class="pre">sklearn.cluster</span></code>: Clustering</a><ul>
<li><a class="reference internal" href="#classes">Classes</a></li>
<li><a class="reference internal" href="#id1">Functions</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.cluster.bicluster"><code class="docutils literal"><span class="pre">sklearn.cluster.bicluster</span></code>: Biclustering</a><ul>
<li><a class="reference internal" href="#id2">Classes</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.compose"><code class="docutils literal"><span class="pre">sklearn.compose</span></code>: Composite Estimators</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.covariance"><code class="docutils literal"><span class="pre">sklearn.covariance</span></code>: Covariance Estimators</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.cross_decomposition"><code class="docutils literal"><span class="pre">sklearn.cross_decomposition</span></code>: Cross decomposition</a></li>
<li><a class="reference internal" href="#module-sklearn.datasets"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>: Datasets</a><ul>
<li><a class="reference internal" href="#loaders">Loaders</a></li>
<li><a class="reference internal" href="#samples-generator">Samples generator</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.decomposition"><code class="docutils literal"><span class="pre">sklearn.decomposition</span></code>: Matrix Decomposition</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.discriminant_analysis"><code class="docutils literal"><span class="pre">sklearn.discriminant_analysis</span></code>: Discriminant Analysis</a></li>
<li><a class="reference internal" href="#module-sklearn.dummy"><code class="docutils literal"><span class="pre">sklearn.dummy</span></code>: Dummy estimators</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.ensemble"><code class="docutils literal"><span class="pre">sklearn.ensemble</span></code>: Ensemble Methods</a><ul>
<li><a class="reference internal" href="#module-sklearn.ensemble.partial_dependence">partial dependence</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.exceptions"><code class="docutils literal"><span class="pre">sklearn.exceptions</span></code>: Exceptions and warnings</a></li>
<li><a class="reference internal" href="#module-sklearn.feature_extraction"><code class="docutils literal"><span class="pre">sklearn.feature_extraction</span></code>: Feature Extraction</a><ul>
<li><a class="reference internal" href="#module-sklearn.feature_extraction.image">From images</a></li>
<li><a class="reference internal" href="#module-sklearn.feature_extraction.text">From text</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.feature_selection"><code class="docutils literal"><span class="pre">sklearn.feature_selection</span></code>: Feature Selection</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.gaussian_process"><code class="docutils literal"><span class="pre">sklearn.gaussian_process</span></code>: Gaussian Processes</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.isotonic"><code class="docutils literal"><span class="pre">sklearn.isotonic</span></code>: Isotonic regression</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.impute"><code class="docutils literal"><span class="pre">sklearn.impute</span></code>: Impute</a></li>
<li><a class="reference internal" href="#module-sklearn.kernel_approximation"><code class="docutils literal"><span class="pre">sklearn.kernel_approximation</span></code> Kernel Approximation</a></li>
<li><a class="reference internal" href="#module-sklearn.kernel_ridge"><code class="docutils literal"><span class="pre">sklearn.kernel_ridge</span></code> Kernel Ridge Regression</a></li>
<li><a class="reference internal" href="#module-sklearn.linear_model"><code class="docutils literal"><span class="pre">sklearn.linear_model</span></code>: Generalized Linear Models</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.manifold"><code class="docutils literal"><span class="pre">sklearn.manifold</span></code>: Manifold Learning</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#sklearn-metrics-metrics"><code class="docutils literal"><span class="pre">sklearn.metrics</span></code>: Metrics</a><ul>
<li><a class="reference internal" href="#model-selection-interface">Model Selection Interface</a></li>
<li><a class="reference internal" href="#classification-metrics">Classification metrics</a></li>
<li><a class="reference internal" href="#regression-metrics">Regression metrics</a></li>
<li><a class="reference internal" href="#multilabel-ranking-metrics">Multilabel ranking metrics</a></li>
<li><a class="reference internal" href="#clustering-metrics">Clustering metrics</a></li>
<li><a class="reference internal" href="#biclustering-metrics">Biclustering metrics</a></li>
<li><a class="reference internal" href="#pairwise-metrics">Pairwise metrics</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.mixture"><code class="docutils literal"><span class="pre">sklearn.mixture</span></code>: Gaussian Mixture Models</a></li>
<li><a class="reference internal" href="#module-sklearn.model_selection"><code class="docutils literal"><span class="pre">sklearn.model_selection</span></code>: Model Selection</a><ul>
<li><a class="reference internal" href="#splitter-classes">Splitter Classes</a></li>
<li><a class="reference internal" href="#splitter-functions">Splitter Functions</a></li>
<li><a class="reference internal" href="#hyper-parameter-optimizers">Hyper-parameter optimizers</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#model-validation">Model validation</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.multiclass"><code class="docutils literal"><span class="pre">sklearn.multiclass</span></code>: Multiclass and multilabel classification</a><ul>
<li><a class="reference internal" href="#multiclass-and-multilabel-classification-strategies">Multiclass and multilabel classification strategies</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.multioutput"><code class="docutils literal"><span class="pre">sklearn.multioutput</span></code>: Multioutput regression and classification</a></li>
<li><a class="reference internal" href="#module-sklearn.naive_bayes"><code class="docutils literal"><span class="pre">sklearn.naive_bayes</span></code>: Naive Bayes</a></li>
<li><a class="reference internal" href="#module-sklearn.neighbors"><code class="docutils literal"><span class="pre">sklearn.neighbors</span></code>: Nearest Neighbors</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.neural_network"><code class="docutils literal"><span class="pre">sklearn.neural_network</span></code>: Neural network models</a></li>
<li><a class="reference internal" href="#module-sklearn.pipeline"><code class="docutils literal"><span class="pre">sklearn.pipeline</span></code>: Pipeline</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.preprocessing"><code class="docutils literal"><span class="pre">sklearn.preprocessing</span></code>: Preprocessing and Normalization</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.random_projection"><code class="docutils literal"><span class="pre">sklearn.random_projection</span></code>: Random projection</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.semi_supervised"><code class="docutils literal"><span class="pre">sklearn.semi_supervised</span></code> Semi-Supervised Learning</a></li>
<li><a class="reference internal" href="#module-sklearn.svm"><code class="docutils literal"><span class="pre">sklearn.svm</span></code>: Support Vector Machines</a><ul>
<li><a class="reference internal" href="#estimators">Estimators</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#low-level-methods">Low-level methods</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.tree"><code class="docutils literal"><span class="pre">sklearn.tree</span></code>: Decision Trees</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.utils"><code class="docutils literal"><span class="pre">sklearn.utils</span></code>: Utilities</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#recently-deprecated">Recently deprecated</a><ul>
<li><a class="reference internal" href="#to-be-removed-in-0-22">To be removed in 0.22</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#to-be-removed-in-0-21">To be removed in 0.21</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#to-be-removed-in-0-20">To be removed in 0.20</a><ul>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
</div>
<input type="checkbox" id="nav-trigger" class="nav-trigger" checked />
<label for="nav-trigger"></label>
<div class="content">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="section" id="api-reference">
<span id="api-ref"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline">¶</a></h1>
<p>This is the class and function reference of scikit-learn. Please refer to
the <a class="reference internal" href="../user_guide.html#user-guide"><span class="std std-ref">full user guide</span></a> for further details, as the class and
function raw specifications may not be enough to give full guidelines on their
uses.
For reference on concepts repeated across the API, see <a class="reference internal" href="../glossary.html#glossary"><span class="std std-ref">Glossary of Common Terms and API Elements</span></a>.</p>
<div class="section" id="module-sklearn.base">
<span id="sklearn-base-base-classes-and-utility-functions"></span><h2><a class="reference internal" href="#module-sklearn.base" title="sklearn.base"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></code></a>: Base classes and utility functions<a class="headerlink" href="#module-sklearn.base" title="Permalink to this headline">¶</a></h2>
<p>Base classes for all estimators.</p>
<div class="section" id="base-classes">
<h3>Base classes<a class="headerlink" href="#base-classes" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator"><code class="xref py py-obj docutils literal"><span class="pre">base.BaseEstimator</span></code></a></td>
<td>Base class for all estimators in scikit-learn</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.base.BiclusterMixin.html#sklearn.base.BiclusterMixin" title="sklearn.base.BiclusterMixin"><code class="xref py py-obj docutils literal"><span class="pre">base.BiclusterMixin</span></code></a></td>
<td>Mixin class for all bicluster estimators in scikit-learn</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin" title="sklearn.base.ClassifierMixin"><code class="xref py py-obj docutils literal"><span class="pre">base.ClassifierMixin</span></code></a></td>
<td>Mixin class for all classifiers in scikit-learn.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.base.ClusterMixin.html#sklearn.base.ClusterMixin" title="sklearn.base.ClusterMixin"><code class="xref py py-obj docutils literal"><span class="pre">base.ClusterMixin</span></code></a></td>
<td>Mixin class for all cluster estimators in scikit-learn.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.base.DensityMixin.html#sklearn.base.DensityMixin" title="sklearn.base.DensityMixin"><code class="xref py py-obj docutils literal"><span class="pre">base.DensityMixin</span></code></a></td>
<td>Mixin class for all density estimators in scikit-learn.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.base.RegressorMixin.html#sklearn.base.RegressorMixin" title="sklearn.base.RegressorMixin"><code class="xref py py-obj docutils literal"><span class="pre">base.RegressorMixin</span></code></a></td>
<td>Mixin class for all regression estimators in scikit-learn.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.base.TransformerMixin.html#sklearn.base.TransformerMixin" title="sklearn.base.TransformerMixin"><code class="xref py py-obj docutils literal"><span class="pre">base.TransformerMixin</span></code></a></td>
<td>Mixin class for all transformers in scikit-learn.</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="functions">
<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.base.clone.html#sklearn.base.clone" title="sklearn.base.clone"><code class="xref py py-obj docutils literal"><span class="pre">base.clone</span></code></a>(estimator[, safe])</td>
<td>Constructs a new estimator with the same parameters.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.base.is_classifier.html#sklearn.base.is_classifier" title="sklearn.base.is_classifier"><code class="xref py py-obj docutils literal"><span class="pre">base.is_classifier</span></code></a>(estimator)</td>
<td>Returns True if the given estimator is (probably) a classifier.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.base.is_regressor.html#sklearn.base.is_regressor" title="sklearn.base.is_regressor"><code class="xref py py-obj docutils literal"><span class="pre">base.is_regressor</span></code></a>(estimator)</td>
<td>Returns True if the given estimator is (probably) a regressor.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.config_context.html#sklearn.config_context" title="sklearn.config_context"><code class="xref py py-obj docutils literal"><span class="pre">config_context</span></code></a>(**new_config)</td>
<td>Context manager for global scikit-learn configuration</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.get_config.html#sklearn.get_config" title="sklearn.get_config"><code class="xref py py-obj docutils literal"><span class="pre">get_config</span></code></a>()</td>
<td>Retrieve current values for configuration set by <a class="reference internal" href="generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal"><span class="pre">set_config</span></code></a></td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-obj docutils literal"><span class="pre">set_config</span></code></a>([assume_finite, working_memory])</td>
<td>Set global scikit-learn configuration</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.calibration">
<span id="sklearn-calibration-probability-calibration"></span><span id="calibration-ref"></span><h2><a class="reference internal" href="#module-sklearn.calibration" title="sklearn.calibration"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.calibration</span></code></a>: Probability Calibration<a class="headerlink" href="#module-sklearn.calibration" title="Permalink to this headline">¶</a></h2>
<p>Calibration of predicted probabilities.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="calibration.html#calibration"><span class="std std-ref">Probability calibration</span></a> section for further details.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.calibration.CalibratedClassifierCV.html#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><code class="xref py py-obj docutils literal"><span class="pre">calibration.CalibratedClassifierCV</span></code></a>([…])</td>
<td>Probability calibration with isotonic regression or sigmoid.</td>
</tr>
</tbody>
</table>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.calibration.calibration_curve.html#sklearn.calibration.calibration_curve" title="sklearn.calibration.calibration_curve"><code class="xref py py-obj docutils literal"><span class="pre">calibration.calibration_curve</span></code></a>(y_true, y_prob)</td>
<td>Compute true and predicted probabilities for a calibration curve.</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.cluster">
<span id="sklearn-cluster-clustering"></span><span id="cluster-ref"></span><h2><a class="reference internal" href="#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></code></a>: Clustering<a class="headerlink" href="#module-sklearn.cluster" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></code></a> module gathers popular unsupervised clustering
algorithms.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="clustering.html#clustering"><span class="std std-ref">Clustering</span></a> section for further details.</p>
<div class="section" id="classes">
<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.AffinityPropagation.html#sklearn.cluster.AffinityPropagation" title="sklearn.cluster.AffinityPropagation"><code class="xref py py-obj docutils literal"><span class="pre">cluster.AffinityPropagation</span></code></a>([damping, …])</td>
<td>Perform Affinity Propagation Clustering of data.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering" title="sklearn.cluster.AgglomerativeClustering"><code class="xref py py-obj docutils literal"><span class="pre">cluster.AgglomerativeClustering</span></code></a>([…])</td>
<td>Agglomerative Clustering</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.Birch.html#sklearn.cluster.Birch" title="sklearn.cluster.Birch"><code class="xref py py-obj docutils literal"><span class="pre">cluster.Birch</span></code></a>([threshold, branching_factor, …])</td>
<td>Implements the Birch clustering algorithm.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN" title="sklearn.cluster.DBSCAN"><code class="xref py py-obj docutils literal"><span class="pre">cluster.DBSCAN</span></code></a>([eps, min_samples, metric, …])</td>
<td>Perform DBSCAN clustering from vector array or distance matrix.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.FeatureAgglomeration.html#sklearn.cluster.FeatureAgglomeration" title="sklearn.cluster.FeatureAgglomeration"><code class="xref py py-obj docutils literal"><span class="pre">cluster.FeatureAgglomeration</span></code></a>([n_clusters, …])</td>
<td>Agglomerate features.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans"><code class="xref py py-obj docutils literal"><span class="pre">cluster.KMeans</span></code></a>([n_clusters, init, n_init, …])</td>
<td>K-Means clustering</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster.MiniBatchKMeans"><code class="xref py py-obj docutils literal"><span class="pre">cluster.MiniBatchKMeans</span></code></a>([n_clusters, init, …])</td>
<td>Mini-Batch K-Means clustering</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cluster.MeanShift.html#sklearn.cluster.MeanShift" title="sklearn.cluster.MeanShift"><code class="xref py py-obj docutils literal"><span class="pre">cluster.MeanShift</span></code></a>([bandwidth, seeds, …])</td>
<td>Mean shift clustering using a flat kernel.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering" title="sklearn.cluster.SpectralClustering"><code class="xref py py-obj docutils literal"><span class="pre">cluster.SpectralClustering</span></code></a>([n_clusters, …])</td>
<td>Apply clustering to a projection to the normalized laplacian.</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="id1">
<h3>Functions<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.affinity_propagation.html#sklearn.cluster.affinity_propagation" title="sklearn.cluster.affinity_propagation"><code class="xref py py-obj docutils literal"><span class="pre">cluster.affinity_propagation</span></code></a>(S[, …])</td>
<td>Perform Affinity Propagation Clustering of data</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cluster.dbscan.html#sklearn.cluster.dbscan" title="sklearn.cluster.dbscan"><code class="xref py py-obj docutils literal"><span class="pre">cluster.dbscan</span></code></a>(X[, eps, min_samples, …])</td>
<td>Perform DBSCAN clustering from vector array or distance matrix.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.estimate_bandwidth.html#sklearn.cluster.estimate_bandwidth" title="sklearn.cluster.estimate_bandwidth"><code class="xref py py-obj docutils literal"><span class="pre">cluster.estimate_bandwidth</span></code></a>(X[, quantile, …])</td>
<td>Estimate the bandwidth to use with the mean-shift algorithm.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cluster.k_means.html#sklearn.cluster.k_means" title="sklearn.cluster.k_means"><code class="xref py py-obj docutils literal"><span class="pre">cluster.k_means</span></code></a>(X, n_clusters[, …])</td>
<td>K-means clustering algorithm.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.mean_shift.html#sklearn.cluster.mean_shift" title="sklearn.cluster.mean_shift"><code class="xref py py-obj docutils literal"><span class="pre">cluster.mean_shift</span></code></a>(X[, bandwidth, seeds, …])</td>
<td>Perform mean shift clustering of data using a flat kernel.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cluster.spectral_clustering.html#sklearn.cluster.spectral_clustering" title="sklearn.cluster.spectral_clustering"><code class="xref py py-obj docutils literal"><span class="pre">cluster.spectral_clustering</span></code></a>(affinity[, …])</td>
<td>Apply clustering to a projection to the normalized laplacian.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.ward_tree.html#sklearn.cluster.ward_tree" title="sklearn.cluster.ward_tree"><code class="xref py py-obj docutils literal"><span class="pre">cluster.ward_tree</span></code></a>(X[, connectivity, …])</td>
<td>Ward clustering based on a Feature matrix.</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.cluster.bicluster">
<span id="sklearn-cluster-bicluster-biclustering"></span><span id="bicluster-ref"></span><h2><a class="reference internal" href="#module-sklearn.cluster.bicluster" title="sklearn.cluster.bicluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster.bicluster</span></code></a>: Biclustering<a class="headerlink" href="#module-sklearn.cluster.bicluster" title="Permalink to this headline">¶</a></h2>
<p>Spectral biclustering algorithms.</p>
<p>Authors : Kemal Eren
License: BSD 3 clause</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="biclustering.html#biclustering"><span class="std std-ref">Biclustering</span></a> section for further details.</p>
<div class="section" id="id2">
<h3>Classes<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cluster.bicluster.SpectralBiclustering.html#sklearn.cluster.bicluster.SpectralBiclustering" title="sklearn.cluster.bicluster.SpectralBiclustering"><code class="xref py py-obj docutils literal"><span class="pre">SpectralBiclustering</span></code></a>([n_clusters, method, …])</td>
<td>Spectral biclustering (Kluger, 2003).</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cluster.bicluster.SpectralCoclustering.html#sklearn.cluster.bicluster.SpectralCoclustering" title="sklearn.cluster.bicluster.SpectralCoclustering"><code class="xref py py-obj docutils literal"><span class="pre">SpectralCoclustering</span></code></a>([n_clusters, …])</td>
<td>Spectral Co-Clustering algorithm (Dhillon, 2001).</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.compose">
<span id="sklearn-compose-composite-estimators"></span><span id="compose-ref"></span><h2><a class="reference internal" href="#module-sklearn.compose" title="sklearn.compose"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.compose</span></code></a>: Composite Estimators<a class="headerlink" href="#module-sklearn.compose" title="Permalink to this headline">¶</a></h2>
<p>Meta-estimators for building composite models with transformers</p>
<p>In addition to its current contents, this module will eventually be home to
refurbished versions of Pipeline and FeatureUnion.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="compose.html#combining-estimators"><span class="std std-ref">Pipelines and composite estimators</span></a> section for further
details.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-obj docutils literal"><span class="pre">compose.ColumnTransformer</span></code></a>(transformers[, …])</td>
<td>Applies transformers to columns of an array or pandas DataFrame.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.compose.TransformedTargetRegressor.html#sklearn.compose.TransformedTargetRegressor" title="sklearn.compose.TransformedTargetRegressor"><code class="xref py py-obj docutils literal"><span class="pre">compose.TransformedTargetRegressor</span></code></a>([…])</td>
<td>Meta-estimator to regress on a transformed target.</td>
</tr>
</tbody>
</table>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.compose.make_column_transformer.html#sklearn.compose.make_column_transformer" title="sklearn.compose.make_column_transformer"><code class="xref py py-obj docutils literal"><span class="pre">compose.make_column_transformer</span></code></a>(…)</td>
<td>Construct a ColumnTransformer from the given transformers.</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.covariance">
<span id="sklearn-covariance-covariance-estimators"></span><span id="covariance-ref"></span><h2><a class="reference internal" href="#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.covariance</span></code></a>: Covariance Estimators<a class="headerlink" href="#module-sklearn.covariance" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.covariance</span></code></a> module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="covariance.html#covariance"><span class="std std-ref">Covariance estimation</span></a> section for further details.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.covariance.EmpiricalCovariance.html#sklearn.covariance.EmpiricalCovariance" title="sklearn.covariance.EmpiricalCovariance"><code class="xref py py-obj docutils literal"><span class="pre">covariance.EmpiricalCovariance</span></code></a>([…])</td>
<td>Maximum likelihood covariance estimator</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope" title="sklearn.covariance.EllipticEnvelope"><code class="xref py py-obj docutils literal"><span class="pre">covariance.EllipticEnvelope</span></code></a>([…])</td>
<td>An object for detecting outliers in a Gaussian distributed dataset.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.covariance.GraphicalLasso.html#sklearn.covariance.GraphicalLasso" title="sklearn.covariance.GraphicalLasso"><code class="xref py py-obj docutils literal"><span class="pre">covariance.GraphicalLasso</span></code></a>([alpha, mode, …])</td>
<td>Sparse inverse covariance estimation with an l1-penalized estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.covariance.GraphicalLassoCV.html#sklearn.covariance.GraphicalLassoCV" title="sklearn.covariance.GraphicalLassoCV"><code class="xref py py-obj docutils literal"><span class="pre">covariance.GraphicalLassoCV</span></code></a>([alphas, …])</td>
<td>Sparse inverse covariance w/ cross-validated choice of the l1 penalty</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.covariance.LedoitWolf.html#sklearn.covariance.LedoitWolf" title="sklearn.covariance.LedoitWolf"><code class="xref py py-obj docutils literal"><span class="pre">covariance.LedoitWolf</span></code></a>([store_precision, …])</td>
<td>LedoitWolf Estimator</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.covariance.MinCovDet.html#sklearn.covariance.MinCovDet" title="sklearn.covariance.MinCovDet"><code class="xref py py-obj docutils literal"><span class="pre">covariance.MinCovDet</span></code></a>([store_precision, …])</td>
<td>Minimum Covariance Determinant (MCD): robust estimator of covariance.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.covariance.OAS.html#sklearn.covariance.OAS" title="sklearn.covariance.OAS"><code class="xref py py-obj docutils literal"><span class="pre">covariance.OAS</span></code></a>([store_precision, …])</td>
<td>Oracle Approximating Shrinkage Estimator</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.covariance.ShrunkCovariance.html#sklearn.covariance.ShrunkCovariance" title="sklearn.covariance.ShrunkCovariance"><code class="xref py py-obj docutils literal"><span class="pre">covariance.ShrunkCovariance</span></code></a>([…])</td>
<td>Covariance estimator with shrinkage</td>
</tr>
</tbody>
</table>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.covariance.empirical_covariance.html#sklearn.covariance.empirical_covariance" title="sklearn.covariance.empirical_covariance"><code class="xref py py-obj docutils literal"><span class="pre">covariance.empirical_covariance</span></code></a>(X[, …])</td>
<td>Computes the Maximum likelihood covariance estimator</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.covariance.graphical_lasso.html#sklearn.covariance.graphical_lasso" title="sklearn.covariance.graphical_lasso"><code class="xref py py-obj docutils literal"><span class="pre">covariance.graphical_lasso</span></code></a>(emp_cov, alpha[, …])</td>
<td>l1-penalized covariance estimator</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.covariance.ledoit_wolf.html#sklearn.covariance.ledoit_wolf" title="sklearn.covariance.ledoit_wolf"><code class="xref py py-obj docutils literal"><span class="pre">covariance.ledoit_wolf</span></code></a>(X[, assume_centered, …])</td>
<td>Estimates the shrunk Ledoit-Wolf covariance matrix.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.covariance.oas.html#sklearn.covariance.oas" title="sklearn.covariance.oas"><code class="xref py py-obj docutils literal"><span class="pre">covariance.oas</span></code></a>(X[, assume_centered])</td>
<td>Estimate covariance with the Oracle Approximating Shrinkage algorithm.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.covariance.shrunk_covariance.html#sklearn.covariance.shrunk_covariance" title="sklearn.covariance.shrunk_covariance"><code class="xref py py-obj docutils literal"><span class="pre">covariance.shrunk_covariance</span></code></a>(emp_cov[, …])</td>
<td>Calculates a covariance matrix shrunk on the diagonal</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.cross_decomposition">
<span id="sklearn-cross-decomposition-cross-decomposition"></span><span id="cross-decomposition-ref"></span><h2><a class="reference internal" href="#module-sklearn.cross_decomposition" title="sklearn.cross_decomposition"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cross_decomposition</span></code></a>: Cross decomposition<a class="headerlink" href="#module-sklearn.cross_decomposition" title="Permalink to this headline">¶</a></h2>
<p><strong>User guide:</strong> See the <a class="reference internal" href="cross_decomposition.html#cross-decomposition"><span class="std std-ref">Cross decomposition</span></a> section for further details.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cross_decomposition.CCA.html#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition.CCA"><code class="xref py py-obj docutils literal"><span class="pre">cross_decomposition.CCA</span></code></a>([n_components, …])</td>
<td>CCA Canonical Correlation Analysis.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cross_decomposition.PLSCanonical.html#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition.PLSCanonical"><code class="xref py py-obj docutils literal"><span class="pre">cross_decomposition.PLSCanonical</span></code></a>([…])</td>
<td>PLSCanonical implements the 2 blocks canonical PLS of the original Wold algorithm [Tenenhaus 1998] p.204, referred as PLS-C2A in [Wegelin 2000].</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.cross_decomposition.PLSRegression.html#sklearn.cross_decomposition.PLSRegression" title="sklearn.cross_decomposition.PLSRegression"><code class="xref py py-obj docutils literal"><span class="pre">cross_decomposition.PLSRegression</span></code></a>([…])</td>
<td>PLS regression</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.cross_decomposition.PLSSVD.html#sklearn.cross_decomposition.PLSSVD" title="sklearn.cross_decomposition.PLSSVD"><code class="xref py py-obj docutils literal"><span class="pre">cross_decomposition.PLSSVD</span></code></a>([n_components, …])</td>
<td>Partial Least Square SVD</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.datasets">
<span id="sklearn-datasets-datasets"></span><span id="datasets-ref"></span><h2><a class="reference internal" href="#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a>: Datasets<a class="headerlink" href="#module-sklearn.datasets" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a> module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="../datasets/index.html#datasets"><span class="std std-ref">Dataset loading utilities</span></a> section for further details.</p>
<div class="section" id="loaders">
<h3>Loaders<a class="headerlink" href="#loaders" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.clear_data_home.html#sklearn.datasets.clear_data_home" title="sklearn.datasets.clear_data_home"><code class="xref py py-obj docutils literal"><span class="pre">datasets.clear_data_home</span></code></a>([data_home])</td>
<td>Delete all the content of the data home cache.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.dump_svmlight_file.html#sklearn.datasets.dump_svmlight_file" title="sklearn.datasets.dump_svmlight_file"><code class="xref py py-obj docutils literal"><span class="pre">datasets.dump_svmlight_file</span></code></a>(X, y, f[, …])</td>
<td>Dump the dataset in svmlight / libsvm file format.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="sklearn.datasets.fetch_20newsgroups"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_20newsgroups</span></code></a>([data_home, …])</td>
<td>Load the filenames and data from the 20 newsgroups dataset.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_20newsgroups_vectorized.html#sklearn.datasets.fetch_20newsgroups_vectorized" title="sklearn.datasets.fetch_20newsgroups_vectorized"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_20newsgroups_vectorized</span></code></a>([…])</td>
<td>Load the 20 newsgroups dataset and transform it into tf-idf vectors.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_california_housing.html#sklearn.datasets.fetch_california_housing" title="sklearn.datasets.fetch_california_housing"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_california_housing</span></code></a>([…])</td>
<td>Loader for the California housing dataset from StatLib.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_covtype.html#sklearn.datasets.fetch_covtype" title="sklearn.datasets.fetch_covtype"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_covtype</span></code></a>([data_home, …])</td>
<td>Load the covertype dataset, downloading it if necessary.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_kddcup99.html#sklearn.datasets.fetch_kddcup99" title="sklearn.datasets.fetch_kddcup99"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_kddcup99</span></code></a>([subset, data_home, …])</td>
<td>Load and return the kddcup 99 dataset (classification).</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_lfw_pairs.html#sklearn.datasets.fetch_lfw_pairs" title="sklearn.datasets.fetch_lfw_pairs"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_lfw_pairs</span></code></a>([subset, …])</td>
<td>Loader for the Labeled Faces in the Wild (LFW) pairs dataset</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people" title="sklearn.datasets.fetch_lfw_people"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_lfw_people</span></code></a>([data_home, …])</td>
<td>Loader for the Labeled Faces in the Wild (LFW) people dataset</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_mldata.html#sklearn.datasets.fetch_mldata" title="sklearn.datasets.fetch_mldata"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_mldata</span></code></a>(dataname[, …])</td>
<td>Fetch an mldata.org data set</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_olivetti_faces.html#sklearn.datasets.fetch_olivetti_faces" title="sklearn.datasets.fetch_olivetti_faces"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_olivetti_faces</span></code></a>([data_home, …])</td>
<td>Loader for the Olivetti faces data-set from AT&T.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_rcv1.html#sklearn.datasets.fetch_rcv1" title="sklearn.datasets.fetch_rcv1"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_rcv1</span></code></a>([data_home, subset, …])</td>
<td>Load the RCV1 multilabel dataset, downloading it if necessary.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.fetch_species_distributions.html#sklearn.datasets.fetch_species_distributions" title="sklearn.datasets.fetch_species_distributions"><code class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_species_distributions</span></code></a>([…])</td>
<td>Loader for species distribution dataset from Phillips et.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.get_data_home.html#sklearn.datasets.get_data_home" title="sklearn.datasets.get_data_home"><code class="xref py py-obj docutils literal"><span class="pre">datasets.get_data_home</span></code></a>([data_home])</td>
<td>Return the path of the scikit-learn data dir.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston" title="sklearn.datasets.load_boston"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_boston</span></code></a>([return_X_y])</td>
<td>Load and return the boston house-prices dataset (regression).</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer" title="sklearn.datasets.load_breast_cancer"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_breast_cancer</span></code></a>([return_X_y])</td>
<td>Load and return the breast cancer wisconsin dataset (classification).</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.load_diabetes.html#sklearn.datasets.load_diabetes" title="sklearn.datasets.load_diabetes"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_diabetes</span></code></a>([return_X_y])</td>
<td>Load and return the diabetes dataset (regression).</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_digits</span></code></a>([n_class, return_X_y])</td>
<td>Load and return the digits dataset (classification).</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.load_files.html#sklearn.datasets.load_files" title="sklearn.datasets.load_files"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_files</span></code></a>(container_path[, …])</td>
<td>Load text files with categories as subfolder names.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_iris</span></code></a>([return_X_y])</td>
<td>Load and return the iris dataset (classification).</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud" title="sklearn.datasets.load_linnerud"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_linnerud</span></code></a>([return_X_y])</td>
<td>Load and return the linnerud dataset (multivariate regression).</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.load_sample_image.html#sklearn.datasets.load_sample_image" title="sklearn.datasets.load_sample_image"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_sample_image</span></code></a>(image_name)</td>
<td>Load the numpy array of a single sample image</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.load_sample_images.html#sklearn.datasets.load_sample_images" title="sklearn.datasets.load_sample_images"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_sample_images</span></code></a>()</td>
<td>Load sample images for image manipulation.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.load_svmlight_file.html#sklearn.datasets.load_svmlight_file" title="sklearn.datasets.load_svmlight_file"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_svmlight_file</span></code></a>(f[, n_features, …])</td>
<td>Load datasets in the svmlight / libsvm format into sparse CSR matrix</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.load_svmlight_files.html#sklearn.datasets.load_svmlight_files" title="sklearn.datasets.load_svmlight_files"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_svmlight_files</span></code></a>(files[, …])</td>
<td>Load dataset from multiple files in SVMlight format</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine" title="sklearn.datasets.load_wine"><code class="xref py py-obj docutils literal"><span class="pre">datasets.load_wine</span></code></a>([return_X_y])</td>
<td>Load and return the wine dataset (classification).</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.mldata_filename.html#sklearn.datasets.mldata_filename" title="sklearn.datasets.mldata_filename"><code class="xref py py-obj docutils literal"><span class="pre">datasets.mldata_filename</span></code></a>(dataname)</td>
<td>Convert a raw name for a data set in a mldata.org filename.</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="samples-generator">
<h3>Samples generator<a class="headerlink" href="#samples-generator" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_biclusters.html#sklearn.datasets.make_biclusters" title="sklearn.datasets.make_biclusters"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_biclusters</span></code></a>(shape, n_clusters)</td>
<td>Generate an array with constant block diagonal structure for biclustering.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_blobs</span></code></a>([n_samples, n_features, …])</td>
<td>Generate isotropic Gaussian blobs for clustering.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_checkerboard.html#sklearn.datasets.make_checkerboard" title="sklearn.datasets.make_checkerboard"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_checkerboard</span></code></a>(shape, n_clusters)</td>
<td>Generate an array with block checkerboard structure for biclustering.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_circles</span></code></a>([n_samples, shuffle, …])</td>
<td>Make a large circle containing a smaller circle in 2d.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_classification</span></code></a>([n_samples, …])</td>
<td>Generate a random n-class classification problem.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_friedman1.html#sklearn.datasets.make_friedman1" title="sklearn.datasets.make_friedman1"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_friedman1</span></code></a>([n_samples, …])</td>
<td>Generate the “Friedman #1” regression problem</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_friedman2.html#sklearn.datasets.make_friedman2" title="sklearn.datasets.make_friedman2"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_friedman2</span></code></a>([n_samples, noise, …])</td>
<td>Generate the “Friedman #2” regression problem</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_friedman3.html#sklearn.datasets.make_friedman3" title="sklearn.datasets.make_friedman3"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_friedman3</span></code></a>([n_samples, noise, …])</td>
<td>Generate the “Friedman #3” regression problem</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_gaussian_quantiles.html#sklearn.datasets.make_gaussian_quantiles" title="sklearn.datasets.make_gaussian_quantiles"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_gaussian_quantiles</span></code></a>([mean, …])</td>
<td>Generate isotropic Gaussian and label samples by quantile</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_hastie_10_2.html#sklearn.datasets.make_hastie_10_2" title="sklearn.datasets.make_hastie_10_2"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_hastie_10_2</span></code></a>([n_samples, …])</td>
<td>Generates data for binary classification used in Hastie et al.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_low_rank_matrix.html#sklearn.datasets.make_low_rank_matrix" title="sklearn.datasets.make_low_rank_matrix"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_low_rank_matrix</span></code></a>([n_samples, …])</td>
<td>Generate a mostly low rank matrix with bell-shaped singular values</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons" title="sklearn.datasets.make_moons"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_moons</span></code></a>([n_samples, shuffle, …])</td>
<td>Make two interleaving half circles</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_multilabel_classification.html#sklearn.datasets.make_multilabel_classification" title="sklearn.datasets.make_multilabel_classification"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_multilabel_classification</span></code></a>([…])</td>
<td>Generate a random multilabel classification problem.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_regression</span></code></a>([n_samples, …])</td>
<td>Generate a random regression problem.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_s_curve.html#sklearn.datasets.make_s_curve" title="sklearn.datasets.make_s_curve"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_s_curve</span></code></a>([n_samples, noise, …])</td>
<td>Generate an S curve dataset.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_sparse_coded_signal.html#sklearn.datasets.make_sparse_coded_signal" title="sklearn.datasets.make_sparse_coded_signal"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_sparse_coded_signal</span></code></a>(n_samples, …)</td>
<td>Generate a signal as a sparse combination of dictionary elements.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_sparse_spd_matrix.html#sklearn.datasets.make_sparse_spd_matrix" title="sklearn.datasets.make_sparse_spd_matrix"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_sparse_spd_matrix</span></code></a>([dim, …])</td>
<td>Generate a sparse symmetric definite positive matrix.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_sparse_uncorrelated.html#sklearn.datasets.make_sparse_uncorrelated" title="sklearn.datasets.make_sparse_uncorrelated"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_sparse_uncorrelated</span></code></a>([…])</td>
<td>Generate a random regression problem with sparse uncorrelated design</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.datasets.make_spd_matrix.html#sklearn.datasets.make_spd_matrix" title="sklearn.datasets.make_spd_matrix"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_spd_matrix</span></code></a>(n_dim[, random_state])</td>
<td>Generate a random symmetric, positive-definite matrix.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.datasets.make_swiss_roll.html#sklearn.datasets.make_swiss_roll" title="sklearn.datasets.make_swiss_roll"><code class="xref py py-obj docutils literal"><span class="pre">datasets.make_swiss_roll</span></code></a>([n_samples, noise, …])</td>
<td>Generate a swiss roll dataset.</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-sklearn.decomposition">
<span id="sklearn-decomposition-matrix-decomposition"></span><span id="decomposition-ref"></span><h2><a class="reference internal" href="#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.decomposition</span></code></a>: Matrix Decomposition<a class="headerlink" href="#module-sklearn.decomposition" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.decomposition</span></code></a> module includes matrix decomposition
algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
this module can be regarded as dimensionality reduction techniques.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="decomposition.html#decompositions"><span class="std std-ref">Decomposing signals in components (matrix factorization problems)</span></a> section for further details.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.decomposition.DictionaryLearning.html#sklearn.decomposition.DictionaryLearning" title="sklearn.decomposition.DictionaryLearning"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.DictionaryLearning</span></code></a>([…])</td>
<td>Dictionary learning</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.decomposition.FactorAnalysis.html#sklearn.decomposition.FactorAnalysis" title="sklearn.decomposition.FactorAnalysis"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.FactorAnalysis</span></code></a>([n_components, …])</td>
<td>Factor Analysis (FA)</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.decomposition.FastICA.html#sklearn.decomposition.FastICA" title="sklearn.decomposition.FastICA"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.FastICA</span></code></a>([n_components, …])</td>
<td>FastICA: a fast algorithm for Independent Component Analysis.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.decomposition.IncrementalPCA.html#sklearn.decomposition.IncrementalPCA" title="sklearn.decomposition.IncrementalPCA"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.IncrementalPCA</span></code></a>([n_components, …])</td>
<td>Incremental principal components analysis (IPCA).</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.decomposition.KernelPCA.html#sklearn.decomposition.KernelPCA" title="sklearn.decomposition.KernelPCA"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.KernelPCA</span></code></a>([n_components, …])</td>
<td>Kernel Principal component analysis (KPCA)</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.decomposition.LatentDirichletAllocation.html#sklearn.decomposition.LatentDirichletAllocation" title="sklearn.decomposition.LatentDirichletAllocation"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.LatentDirichletAllocation</span></code></a>([…])</td>
<td>Latent Dirichlet Allocation with online variational Bayes algorithm</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.decomposition.MiniBatchDictionaryLearning.html#sklearn.decomposition.MiniBatchDictionaryLearning" title="sklearn.decomposition.MiniBatchDictionaryLearning"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.MiniBatchDictionaryLearning</span></code></a>([…])</td>
<td>Mini-batch dictionary learning</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.decomposition.MiniBatchSparsePCA.html#sklearn.decomposition.MiniBatchSparsePCA" title="sklearn.decomposition.MiniBatchSparsePCA"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.MiniBatchSparsePCA</span></code></a>([…])</td>
<td>Mini-batch Sparse Principal Components Analysis</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF" title="sklearn.decomposition.NMF"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.NMF</span></code></a>([n_components, init, …])</td>
<td>Non-Negative Matrix Factorization (NMF)</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.PCA</span></code></a>([n_components, copy, …])</td>
<td>Principal component analysis (PCA)</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.decomposition.SparsePCA.html#sklearn.decomposition.SparsePCA" title="sklearn.decomposition.SparsePCA"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.SparsePCA</span></code></a>([n_components, …])</td>
<td>Sparse Principal Components Analysis (SparsePCA)</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.decomposition.SparseCoder.html#sklearn.decomposition.SparseCoder" title="sklearn.decomposition.SparseCoder"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.SparseCoder</span></code></a>(dictionary[, …])</td>
<td>Sparse coding</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.decomposition.TruncatedSVD.html#sklearn.decomposition.TruncatedSVD" title="sklearn.decomposition.TruncatedSVD"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.TruncatedSVD</span></code></a>([n_components, …])</td>
<td>Dimensionality reduction using truncated SVD (aka LSA).</td>
</tr>
</tbody>
</table>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.decomposition.dict_learning.html#sklearn.decomposition.dict_learning" title="sklearn.decomposition.dict_learning"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.dict_learning</span></code></a>(X, n_components, …)</td>
<td>Solves a dictionary learning matrix factorization problem.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.decomposition.dict_learning_online.html#sklearn.decomposition.dict_learning_online" title="sklearn.decomposition.dict_learning_online"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.dict_learning_online</span></code></a>(X[, …])</td>
<td>Solves a dictionary learning matrix factorization problem online.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.decomposition.fastica.html#sklearn.decomposition.fastica" title="sklearn.decomposition.fastica"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.fastica</span></code></a>(X[, n_components, …])</td>
<td>Perform Fast Independent Component Analysis.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.decomposition.sparse_encode.html#sklearn.decomposition.sparse_encode" title="sklearn.decomposition.sparse_encode"><code class="xref py py-obj docutils literal"><span class="pre">decomposition.sparse_encode</span></code></a>(X, dictionary[, …])</td>
<td>Sparse coding</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.discriminant_analysis">
<span id="sklearn-discriminant-analysis-discriminant-analysis"></span><span id="lda-ref"></span><h2><a class="reference internal" href="#module-sklearn.discriminant_analysis" title="sklearn.discriminant_analysis"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.discriminant_analysis</span></code></a>: Discriminant Analysis<a class="headerlink" href="#module-sklearn.discriminant_analysis" title="Permalink to this headline">¶</a></h2>
<p>Linear Discriminant Analysis and Quadratic Discriminant Analysis</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="lda_qda.html#lda-qda"><span class="std std-ref">Linear and Quadratic Discriminant Analysis</span></a> section for further details.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-obj docutils literal"><span class="pre">discriminant_analysis.LinearDiscriminantAnalysis</span></code></a>([…])</td>
<td>Linear Discriminant Analysis</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis"><code class="xref py py-obj docutils literal"><span class="pre">discriminant_analysis.QuadraticDiscriminantAnalysis</span></code></a>([…])</td>
<td>Quadratic Discriminant Analysis</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.dummy">
<span id="sklearn-dummy-dummy-estimators"></span><span id="dummy-ref"></span><h2><a class="reference internal" href="#module-sklearn.dummy" title="sklearn.dummy"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.dummy</span></code></a>: Dummy estimators<a class="headerlink" href="#module-sklearn.dummy" title="Permalink to this headline">¶</a></h2>
<p><strong>User guide:</strong> See the <a class="reference internal" href="model_evaluation.html#model-evaluation"><span class="std std-ref">Model evaluation: quantifying the quality of predictions</span></a> section for further details.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier"><code class="xref py py-obj docutils literal"><span class="pre">dummy.DummyClassifier</span></code></a>([strategy, …])</td>
<td>DummyClassifier is a classifier that makes predictions using simple rules.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.dummy.DummyRegressor.html#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><code class="xref py py-obj docutils literal"><span class="pre">dummy.DummyRegressor</span></code></a>([strategy, constant, …])</td>
<td>DummyRegressor is a regressor that makes predictions using simple rules.</td>
</tr>
</tbody>
</table>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
</tbody>
</table>
</div>
<div class="section" id="module-sklearn.ensemble">
<span id="sklearn-ensemble-ensemble-methods"></span><span id="ensemble-ref"></span><h2><a class="reference internal" href="#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.ensemble</span></code></a>: Ensemble Methods<a class="headerlink" href="#module-sklearn.ensemble" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.ensemble</span></code></a> module includes ensemble-based methods for
classification, regression and anomaly detection.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="ensemble.html#ensemble"><span class="std std-ref">Ensemble methods</span></a> section for further details.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble.AdaBoostClassifier"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.AdaBoostClassifier</span></code></a>([…])</td>
<td>An AdaBoost classifier.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble.AdaBoostRegressor"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.AdaBoostRegressor</span></code></a>([base_estimator, …])</td>
<td>An AdaBoost regressor.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble.BaggingClassifier"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.BaggingClassifier</span></code></a>([base_estimator, …])</td>
<td>A Bagging classifier.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.ensemble.BaggingRegressor.html#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble.BaggingRegressor"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.BaggingRegressor</span></code></a>([base_estimator, …])</td>
<td>A Bagging regressor.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier" title="sklearn.ensemble.ExtraTreesClassifier"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.ExtraTreesClassifier</span></code></a>([…])</td>
<td>An extra-trees classifier.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble.ExtraTreesRegressor"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.ExtraTreesRegressor</span></code></a>([n_estimators, …])</td>
<td>An extra-trees regressor.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.GradientBoostingClassifier</span></code></a>([loss, …])</td>
<td>Gradient Boosting for classification.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.GradientBoostingRegressor</span></code></a>([loss, …])</td>
<td>Gradient Boosting for regression.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest" title="sklearn.ensemble.IsolationForest"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.IsolationForest</span></code></a>([n_estimators, …])</td>
<td>Isolation Forest Algorithm</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.RandomForestClassifier</span></code></a>([…])</td>
<td>A random forest classifier.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.RandomForestRegressor</span></code></a>([…])</td>
<td>A random forest regressor.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble.RandomTreesEmbedding"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.RandomTreesEmbedding</span></code></a>([…])</td>
<td>An ensemble of totally random trees.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.VotingClassifier</span></code></a>(estimators[, …])</td>
<td>Soft Voting/Majority Rule classifier for unfitted estimators.</td>
</tr>
</tbody>
</table>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
</tbody>
</table>
<div class="section" id="module-sklearn.ensemble.partial_dependence">
<span id="partial-dependence"></span><h3>partial dependence<a class="headerlink" href="#module-sklearn.ensemble.partial_dependence" title="Permalink to this headline">¶</a></h3>
<p>Partial dependence plots for tree ensembles.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/sklearn.ensemble.partial_dependence.partial_dependence.html#sklearn.ensemble.partial_dependence.partial_dependence" title="sklearn.ensemble.partial_dependence.partial_dependence"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.partial_dependence.partial_dependence</span></code></a>(…)</td>
<td>Partial dependence of <code class="docutils literal"><span class="pre">target_variables</span></code>.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/sklearn.ensemble.partial_dependence.plot_partial_dependence.html#sklearn.ensemble.partial_dependence.plot_partial_dependence" title="sklearn.ensemble.partial_dependence.plot_partial_dependence"><code class="xref py py-obj docutils literal"><span class="pre">ensemble.partial_dependence.plot_partial_dependence</span></code></a>(…)</td>
<td>Partial dependence plots for <code class="docutils literal"><span class="pre">features</span></code>.</td>
</tr>
</tbody>