-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathgrid_search.html
1624 lines (1388 loc) · 130 KB
/
grid_search.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>
<html lang="en" data-content_root="../" >
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<meta property="og:title" content="3.2. Tuning the hyper-parameters of an estimator" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://scikit-learn/stable/modules/grid_search.html" />
<meta property="og:site_name" content="scikit-learn" />
<meta property="og:description" content="Hyper-parameters are parameters that are not directly learnt within estimators. In scikit-learn they are passed as arguments to the constructor of the estimator classes. Typical examples include C,..." />
<meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_successive_halving_iterations_001.png" />
<meta property="og:image:alt" content="scikit-learn" />
<meta name="description" content="Hyper-parameters are parameters that are not directly learnt within estimators. In scikit-learn they are passed as arguments to the constructor of the estimator classes. Typical examples include C,..." />
<title>3.2. Tuning the hyper-parameters of an estimator — scikit-learn 1.5.2 documentation</title>
<script data-cfasync="false">
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
</script>
<!-- Loaded before other Sphinx assets -->
<link href="../_static/styles/theme.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link href="../_static/styles/bootstrap.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link href="../_static/styles/pydata-sphinx-theme.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link href="../_static/vendor/fontawesome/6.5.2/css/all.min.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="../_static/vendor/fontawesome/6.5.2/webfonts/fa-solid-900.woff2" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="../_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.woff2" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="../_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.woff2" />
<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
<link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
<link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
<link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
<link rel="stylesheet" type="text/css" href="../_static/jupyterlite_sphinx.css?v=ca70e7f1" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=d2d258e8" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
<link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="../_static/styles/colors.css?v=cc94ab7d" />
<link rel="stylesheet" type="text/css" href="../_static/styles/custom.css?v=e4cb1417" />
<!-- Pre-loaded scripts that we'll load fully later -->
<link rel="preload" as="script" href="../_static/scripts/bootstrap.js?digest=dfe6caa3a7d634c4db9b" />
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
<script src="../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
<script src="../_static/documentation_options.js?v=73275c37"></script>
<script src="../_static/doctools.js?v=9a2dae69"></script>
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="../_static/clipboard.min.js?v=a7894cd8"></script>
<script src="../_static/copybutton.js?v=97f0b27d"></script>
<script src="../_static/jupyterlite_sphinx.js?v=d6bdf5f8"></script>
<script src="../_static/design-tabs.js?v=f930bc37"></script>
<script data-domain="scikit-learn.org" defer="defer" src="https://views.scientific-python.org/js/script.js"></script>
<script>DOCUMENTATION_OPTIONS.pagename = 'modules/grid_search';</script>
<script>
DOCUMENTATION_OPTIONS.theme_version = '0.15.4';
DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://scikit-learn.org/dev/_static/versions.json';
DOCUMENTATION_OPTIONS.theme_switcher_version_match = '1.5.2';
DOCUMENTATION_OPTIONS.show_version_warning_banner = true;
</script>
<script src="../_static/scripts/dropdown.js?v=e2048168"></script>
<script src="../_static/scripts/version-switcher.js?v=a6dd8357"></script>
<link rel="canonical" href="https://scikit-learn.org/stable/modules/grid_search.html" />
<link rel="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="3.3. Tuning the decision threshold for class prediction" href="classification_threshold.html" />
<link rel="prev" title="3.1. Cross-validation: evaluating estimator performance" href="cross_validation.html" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
</head>
<body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
<div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
<div id="pst-scroll-pixel-helper"></div>
<button type="button" class="btn rounded-pill" id="pst-back-to-top">
<i class="fa-solid fa-arrow-up"></i>Back to top</button>
<input type="checkbox"
class="sidebar-toggle"
id="pst-primary-sidebar-checkbox"/>
<label class="overlay overlay-primary" for="pst-primary-sidebar-checkbox"></label>
<input type="checkbox"
class="sidebar-toggle"
id="pst-secondary-sidebar-checkbox"/>
<label class="overlay overlay-secondary" for="pst-secondary-sidebar-checkbox"></label>
<div class="search-button__wrapper">
<div class="search-button__overlay"></div>
<div class="search-button__search-container">
<form class="bd-search d-flex align-items-center"
action="../search.html"
method="get">
<i class="fa-solid fa-magnifying-glass"></i>
<input type="search"
class="form-control"
name="q"
id="search-input"
placeholder="Search the docs ..."
aria-label="Search the docs ..."
autocomplete="off"
autocorrect="off"
autocapitalize="off"
spellcheck="false"/>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
</form></div>
</div>
<div class="pst-async-banner-revealer d-none">
<aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
</div>
<header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
<div class="bd-header__inner bd-page-width">
<button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
<span class="fa-solid fa-bars"></span>
</button>
<div class=" navbar-header-items__start">
<div class="navbar-item">
<a class="navbar-brand logo" href="../index.html">
<img src="../_static/scikit-learn-logo-small.png" class="logo__image only-light" alt="scikit-learn homepage"/>
<script>document.write(`<img src="../_static/scikit-learn-logo-small.png" class="logo__image only-dark" alt="scikit-learn homepage"/>`);</script>
</a></div>
</div>
<div class=" navbar-header-items">
<div class="me-auto navbar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item ">
<a class="nav-link nav-internal" href="../install.html">
Install
</a>
</li>
<li class="nav-item current active">
<a class="nav-link nav-internal" href="../user_guide.html">
User Guide
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../api/index.html">
API
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../auto_examples/index.html">
Examples
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://blog.scikit-learn.org/">
Community
</a>
</li>
<li class="nav-item dropdown">
<button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-controls="pst-nav-more-links">
More
</button>
<ul id="pst-nav-more-links" class="dropdown-menu">
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../getting_started.html">
Getting Started
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../whats_new.html">
Release History
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../glossary.html">
Glossary
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-external" href="https://scikit-learn.org/dev/developers/index.html">
Development
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../faq.html">
FAQ
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../support.html">
Support
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../related_projects.html">
Related Projects
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../roadmap.html">
Roadmap
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../governance.html">
Governance
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../about.html">
About us
</a>
</li>
</ul>
</li>
</ul>
</nav></div>
</div>
<div class="navbar-header-items__end">
<div class="navbar-item navbar-persistent--container">
<script>
document.write(`
<button class="btn btn-sm pst-navbar-icon search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass fa-lg"></i>
</button>
`);
</script>
</div>
<div class="navbar-item">
<script>
document.write(`
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"></i>
</button>
`);
</script></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/scikit-learn/scikit-learn" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
<div class="navbar-item">
<script>
document.write(`
<div class="version-switcher__container dropdown">
<button id="pst-version-switcher-button-2"
type="button"
class="version-switcher__button btn btn-sm dropdown-toggle"
data-bs-toggle="dropdown"
aria-haspopup="listbox"
aria-controls="pst-version-switcher-list-2"
aria-label="Version switcher list"
>
Choose version <!-- this text may get changed later by javascript -->
<span class="caret"></span>
</button>
<div id="pst-version-switcher-list-2"
class="version-switcher__menu dropdown-menu list-group-flush py-0"
role="listbox" aria-labelledby="pst-version-switcher-button-2">
<!-- dropdown will be populated by javascript on page load -->
</div>
</div>
`);
</script></div>
</div>
</div>
<div class="navbar-persistent--mobile">
<script>
document.write(`
<button class="btn btn-sm pst-navbar-icon search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass fa-lg"></i>
</button>
`);
</script>
</div>
<button class="pst-navbar-icon sidebar-toggle secondary-toggle" aria-label="On this page">
<span class="fa-solid fa-outdent"></span>
</button>
</div>
</header>
<div class="bd-container">
<div class="bd-container__inner bd-page-width">
<div class="bd-sidebar-primary bd-sidebar">
<div class="sidebar-header-items sidebar-primary__section">
<div class="sidebar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item ">
<a class="nav-link nav-internal" href="../install.html">
Install
</a>
</li>
<li class="nav-item current active">
<a class="nav-link nav-internal" href="../user_guide.html">
User Guide
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../api/index.html">
API
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../auto_examples/index.html">
Examples
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://blog.scikit-learn.org/">
Community
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../getting_started.html">
Getting Started
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../whats_new.html">
Release History
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../glossary.html">
Glossary
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://scikit-learn.org/dev/developers/index.html">
Development
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../faq.html">
FAQ
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../support.html">
Support
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../related_projects.html">
Related Projects
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../roadmap.html">
Roadmap
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../governance.html">
Governance
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../about.html">
About us
</a>
</li>
</ul>
</nav></div>
</div>
<div class="sidebar-header-items__end">
<div class="navbar-item">
<script>
document.write(`
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"></i>
</button>
`);
</script></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/scikit-learn/scikit-learn" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
<div class="navbar-item">
<script>
document.write(`
<div class="version-switcher__container dropdown">
<button id="pst-version-switcher-button-3"
type="button"
class="version-switcher__button btn btn-sm dropdown-toggle"
data-bs-toggle="dropdown"
aria-haspopup="listbox"
aria-controls="pst-version-switcher-list-3"
aria-label="Version switcher list"
>
Choose version <!-- this text may get changed later by javascript -->
<span class="caret"></span>
</button>
<div id="pst-version-switcher-list-3"
class="version-switcher__menu dropdown-menu list-group-flush py-0"
role="listbox" aria-labelledby="pst-version-switcher-button-3">
<!-- dropdown will be populated by javascript on page load -->
</div>
</div>
`);
</script></div>
</div>
</div>
<div class="sidebar-primary-items__start sidebar-primary__section">
<div class="sidebar-primary-item">
<nav class="bd-docs-nav bd-links"
aria-label="Section Navigation">
<p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
<div class="bd-toc-item navbar-nav"><ul class="current nav bd-sidenav">
<li class="toctree-l1 has-children"><a class="reference internal" href="../supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="../model_selection.html">3. Model selection and evaluation</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2 current active"><a class="current reference internal" href="#">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="permutation_importance.html">4.2. Permutation feature importance</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../visualizations.html">5. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="array_api.html">11.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">13. External Resources, Videos and Talks</a></li>
</ul>
</div>
</nav></div>
</div>
<div class="sidebar-primary-items__end sidebar-primary__section">
</div>
<div id="rtd-footer-container"></div>
</div>
<main id="main-content" class="bd-main" role="main">
<div class="bd-content">
<div class="bd-article-container">
<div class="bd-header-article d-print-none">
<div class="header-article-items header-article__inner">
<div class="header-article-items__start">
<div class="header-article-item">
<nav aria-label="Breadcrumb" class="d-print-none">
<ul class="bd-breadcrumbs">
<li class="breadcrumb-item breadcrumb-home">
<a href="../index.html" class="nav-link" aria-label="Home">
<i class="fa-solid fa-home"></i>
</a>
</li>
<li class="breadcrumb-item"><a href="../user_guide.html" class="nav-link">User Guide</a></li>
<li class="breadcrumb-item"><a href="../model_selection.html" class="nav-link"><span class="section-number">3. </span>Model selection and evaluation</a></li>
<li class="breadcrumb-item active" aria-current="page"><span...</li>
</ul>
</nav>
</div>
</div>
</div>
</div>
<div id="searchbox"></div>
<article class="bd-article">
<section id="tuning-the-hyper-parameters-of-an-estimator">
<span id="grid-search"></span><h1><span class="section-number">3.2. </span>Tuning the hyper-parameters of an estimator<a class="headerlink" href="#tuning-the-hyper-parameters-of-an-estimator" title="Link to this heading">#</a></h1>
<p>Hyper-parameters are parameters that are not directly learnt within estimators.
In scikit-learn they are passed as arguments to the constructor of the
estimator classes. Typical examples include <code class="docutils literal notranslate"><span class="pre">C</span></code>, <code class="docutils literal notranslate"><span class="pre">kernel</span></code> and <code class="docutils literal notranslate"><span class="pre">gamma</span></code>
for Support Vector Classifier, <code class="docutils literal notranslate"><span class="pre">alpha</span></code> for Lasso, etc.</p>
<p>It is possible and recommended to search the hyper-parameter space for the
best <a class="reference internal" href="cross_validation.html#cross-validation"><span class="std std-ref">cross validation</span></a> score.</p>
<p>Any parameter provided when constructing an estimator may be optimized in this
manner. Specifically, to find the names and current values for all parameters
for a given estimator, use:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">estimator</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
</pre></div>
</div>
<p>A search consists of:</p>
<ul class="simple">
<li><p>an estimator (regressor or classifier such as <code class="docutils literal notranslate"><span class="pre">sklearn.svm.SVC()</span></code>);</p></li>
<li><p>a parameter space;</p></li>
<li><p>a method for searching or sampling candidates;</p></li>
<li><p>a cross-validation scheme; and</p></li>
<li><p>a <a class="reference internal" href="#gridsearch-scoring"><span class="std std-ref">score function</span></a>.</p></li>
</ul>
<p>Two generic approaches to parameter search are provided in
scikit-learn: for given values, <a class="reference internal" href="generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a> exhaustively considers
all parameter combinations, while <a class="reference internal" href="generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomizedSearchCV</span></code></a> can sample a
given number of candidates from a parameter space with a specified
distribution. Both these tools have successive halving counterparts
<a class="reference internal" href="generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a> and <a class="reference internal" href="generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingRandomSearchCV</span></code></a>, which can be
much faster at finding a good parameter combination.</p>
<p>After describing these tools we detail <a class="reference internal" href="#grid-search-tips"><span class="std std-ref">best practices</span></a> applicable to these approaches. Some models allow for
specialized, efficient parameter search strategies, outlined in
<a class="reference internal" href="#alternative-cv"><span class="std std-ref">Alternatives to brute force parameter search</span></a>.</p>
<p>Note that it is common that a small subset of those parameters can have a large
impact on the predictive or computation performance of the model while others
can be left to their default values. It is recommended to read the docstring of
the estimator class to get a finer understanding of their expected behavior,
possibly by reading the enclosed reference to the literature.</p>
<section id="exhaustive-grid-search">
<h2><span class="section-number">3.2.1. </span>Exhaustive Grid Search<a class="headerlink" href="#exhaustive-grid-search" title="Link to this heading">#</a></h2>
<p>The grid search provided by <a class="reference internal" href="generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a> exhaustively generates
candidates from a grid of parameter values specified with the <code class="docutils literal notranslate"><span class="pre">param_grid</span></code>
parameter. For instance, the following <code class="docutils literal notranslate"><span class="pre">param_grid</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">param_grid</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">],</span> <span class="s1">'kernel'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'linear'</span><span class="p">]},</span>
<span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">],</span> <span class="s1">'gamma'</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.0001</span><span class="p">],</span> <span class="s1">'kernel'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'rbf'</span><span class="p">]},</span>
<span class="p">]</span>
</pre></div>
</div>
<p>specifies that two grids should be explored: one with a linear kernel and
C values in [1, 10, 100, 1000], and the second one with an RBF kernel,
and the cross-product of C values ranging in [1, 10, 100, 1000] and gamma
values in [0.001, 0.0001].</p>
<p>The <a class="reference internal" href="generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a> instance implements the usual estimator API: when
“fitting” it on a dataset all the possible combinations of parameter values are
evaluated and the best combination is retained.</p>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p>See <a class="reference internal" href="../auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a>
for an example of Grid Search within a cross validation loop on the iris
dataset. This is the best practice for evaluating the performance of a
model with grid search.</p></li>
<li><p>See <a class="reference internal" href="../auto_examples/model_selection/plot_grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-plot-grid-search-text-feature-extraction-py"><span class="std std-ref">Sample pipeline for text feature extraction and evaluation</span></a> for an example
of Grid Search coupling parameters from a text documents feature
extractor (n-gram count vectorizer and TF-IDF transformer) with a
classifier (here a linear SVM trained with SGD with either elastic
net or L2 penalty) using a <a class="reference internal" href="generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> instance.</p></li>
</ul>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="advanced-examples">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Advanced examples<a class="headerlink" href="#advanced-examples" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<ul class="simple">
<li><p class="sd-card-text">See <a class="reference internal" href="../auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a>
for an example of Grid Search within a cross validation loop on the iris
dataset. This is the best practice for evaluating the performance of a
model with grid search.</p></li>
<li><p class="sd-card-text">See <a class="reference internal" href="../auto_examples/model_selection/plot_multi_metric_evaluation.html#sphx-glr-auto-examples-model-selection-plot-multi-metric-evaluation-py"><span class="std std-ref">Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV</span></a>
for an example of <a class="reference internal" href="generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a> being used to evaluate multiple
metrics simultaneously.</p></li>
<li><p class="sd-card-text">See <a class="reference internal" href="../auto_examples/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py"><span class="std std-ref">Balance model complexity and cross-validated score</span></a>
for an example of using <code class="docutils literal notranslate"><span class="pre">refit=callable</span></code> interface in
<a class="reference internal" href="generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>. The example shows how this interface adds certain
amount of flexibility in identifying the “best” estimator. This interface
can also be used in multiple metrics evaluation.</p></li>
<li><p class="sd-card-text">See <a class="reference internal" href="../auto_examples/model_selection/plot_grid_search_stats.html#sphx-glr-auto-examples-model-selection-plot-grid-search-stats-py"><span class="std std-ref">Statistical comparison of models using grid search</span></a>
for an example of how to do a statistical comparison on the outputs of
<a class="reference internal" href="generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>.</p></li>
</ul>
</div>
</details></section>
<section id="randomized-parameter-optimization">
<span id="randomized-parameter-search"></span><h2><span class="section-number">3.2.2. </span>Randomized Parameter Optimization<a class="headerlink" href="#randomized-parameter-optimization" title="Link to this heading">#</a></h2>
<p>While using a grid of parameter settings is currently the most widely used
method for parameter optimization, other search methods have more
favorable properties.
<a class="reference internal" href="generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomizedSearchCV</span></code></a> implements a randomized search over parameters,
where each setting is sampled from a distribution over possible parameter values.
This has two main benefits over an exhaustive search:</p>
<ul class="simple">
<li><p>A budget can be chosen independent of the number of parameters and possible values.</p></li>
<li><p>Adding parameters that do not influence the performance does not decrease efficiency.</p></li>
</ul>
<p>Specifying how parameters should be sampled is done using a dictionary, very
similar to specifying parameters for <a class="reference internal" href="generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>. Additionally,
a computation budget, being the number of sampled candidates or sampling
iterations, is specified using the <code class="docutils literal notranslate"><span class="pre">n_iter</span></code> parameter.
For each parameter, either a distribution over possible values or a list of
discrete choices (which will be sampled uniformly) can be specified:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">expon</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="mi">100</span><span class="p">),</span> <span class="s1">'gamma'</span><span class="p">:</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">expon</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="mf">.1</span><span class="p">),</span>
<span class="s1">'kernel'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'rbf'</span><span class="p">],</span> <span class="s1">'class_weight'</span><span class="p">:[</span><span class="s1">'balanced'</span><span class="p">,</span> <span class="kc">None</span><span class="p">]}</span>
</pre></div>
</div>
<p>This example uses the <code class="docutils literal notranslate"><span class="pre">scipy.stats</span></code> module, which contains many useful
distributions for sampling parameters, such as <code class="docutils literal notranslate"><span class="pre">expon</span></code>, <code class="docutils literal notranslate"><span class="pre">gamma</span></code>,
<code class="docutils literal notranslate"><span class="pre">uniform</span></code>, <code class="docutils literal notranslate"><span class="pre">loguniform</span></code> or <code class="docutils literal notranslate"><span class="pre">randint</span></code>.</p>
<p>In principle, any function can be passed that provides a <code class="docutils literal notranslate"><span class="pre">rvs</span></code> (random
variate sample) method to sample a value. A call to the <code class="docutils literal notranslate"><span class="pre">rvs</span></code> function should
provide independent random samples from possible parameter values on
consecutive calls.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The distributions in <code class="docutils literal notranslate"><span class="pre">scipy.stats</span></code> prior to version scipy 0.16
do not allow specifying a random state. Instead, they use the global
numpy random state, that can be seeded via <code class="docutils literal notranslate"><span class="pre">np.random.seed</span></code> or set
using <code class="docutils literal notranslate"><span class="pre">np.random.set_state</span></code>. However, beginning scikit-learn 0.18,
the <a class="reference internal" href="../api/sklearn.model_selection.html#module-sklearn.model_selection" title="sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a> module sets the random state provided
by the user if scipy >= 0.16 is also available.</p>
</div>
<p>For continuous parameters, such as <code class="docutils literal notranslate"><span class="pre">C</span></code> above, it is important to specify
a continuous distribution to take full advantage of the randomization. This way,
increasing <code class="docutils literal notranslate"><span class="pre">n_iter</span></code> will always lead to a finer search.</p>
<p>A continuous log-uniform random variable is the continuous version of
a log-spaced parameter. For example to specify the equivalent of <code class="docutils literal notranslate"><span class="pre">C</span></code> from above,
<code class="docutils literal notranslate"><span class="pre">loguniform(1,</span> <span class="pre">100)</span></code> can be used instead of <code class="docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">10,</span> <span class="pre">100]</span></code>.</p>
<p>Mirroring the example above in grid search, we can specify a continuous random
variable that is log-uniformly distributed between <code class="docutils literal notranslate"><span class="pre">1e0</span></code> and <code class="docutils literal notranslate"><span class="pre">1e3</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils.fixes</span> <span class="kn">import</span> <span class="n">loguniform</span>
<span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="n">loguniform</span><span class="p">(</span><span class="mf">1e0</span><span class="p">,</span> <span class="mf">1e3</span><span class="p">),</span>
<span class="s1">'gamma'</span><span class="p">:</span> <span class="n">loguniform</span><span class="p">(</span><span class="mf">1e-4</span><span class="p">,</span> <span class="mf">1e-3</span><span class="p">),</span>
<span class="s1">'kernel'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'rbf'</span><span class="p">],</span>
<span class="s1">'class_weight'</span><span class="p">:[</span><span class="s1">'balanced'</span><span class="p">,</span> <span class="kc">None</span><span class="p">]}</span>
</pre></div>
</div>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p><a class="reference internal" href="../auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py"><span class="std std-ref">Comparing randomized search and grid search for hyperparameter estimation</span></a> compares the usage and efficiency
of randomized search and grid search.</p></li>
</ul>
<p class="rubric">References</p>
<ul class="simple">
<li><p>Bergstra, J. and Bengio, Y.,
Random search for hyper-parameter optimization,
The Journal of Machine Learning Research (2012)</p></li>
</ul>
</section>
<section id="searching-for-optimal-parameters-with-successive-halving">
<span id="successive-halving-user-guide"></span><h2><span class="section-number">3.2.3. </span>Searching for optimal parameters with successive halving<a class="headerlink" href="#searching-for-optimal-parameters-with-successive-halving" title="Link to this heading">#</a></h2>
<p>Scikit-learn also provides the <a class="reference internal" href="generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a> and
<a class="reference internal" href="generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingRandomSearchCV</span></code></a> estimators that can be used to
search a parameter space using successive halving <a class="footnote-reference brackets" href="#id3" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> <a class="footnote-reference brackets" href="#id4" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. Successive
halving (SH) is like a tournament among candidate parameter combinations.
SH is an iterative selection process where all candidates (the
parameter combinations) are evaluated with a small amount of resources at
the first iteration. Only some of these candidates are selected for the next
iteration, which will be allocated more resources. For parameter tuning, the
resource is typically the number of training samples, but it can also be an
arbitrary numeric parameter such as <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> in a random forest.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The resource increase chosen should be large enough so that a large improvement
in scores is obtained when taking into account statistical significance.</p>
</div>
<p>As illustrated in the figure below, only a subset of candidates
‘survive’ until the last iteration. These are the candidates that have
consistently ranked among the top-scoring candidates across all iterations.
Each iteration is allocated an increasing amount of resources per candidate,
here the number of samples.</p>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/model_selection/plot_successive_halving_iterations.html"><img alt="../_images/sphx_glr_plot_successive_halving_iterations_001.png" src="../_images/sphx_glr_plot_successive_halving_iterations_001.png" />
</a>
</figure>
<p>We here briefly describe the main parameters, but each parameter and their
interactions are described more in detail in the dropdown section below. The
<code class="docutils literal notranslate"><span class="pre">factor</span></code> (> 1) parameter controls the rate at which the resources grow, and
the rate at which the number of candidates decreases. In each iteration, the
number of resources per candidate is multiplied by <code class="docutils literal notranslate"><span class="pre">factor</span></code> and the number
of candidates is divided by the same factor. Along with <code class="docutils literal notranslate"><span class="pre">resource</span></code> and
<code class="docutils literal notranslate"><span class="pre">min_resources</span></code>, <code class="docutils literal notranslate"><span class="pre">factor</span></code> is the most important parameter to control the
search in our implementation, though a value of 3 usually works well.
<code class="docutils literal notranslate"><span class="pre">factor</span></code> effectively controls the number of iterations in
<a class="reference internal" href="generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a> and the number of candidates (by default) and
iterations in <a class="reference internal" href="generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingRandomSearchCV</span></code></a>. <code class="docutils literal notranslate"><span class="pre">aggressive_elimination=True</span></code>
can also be used if the number of available resources is small. More control
is available through tuning the <code class="docutils literal notranslate"><span class="pre">min_resources</span></code> parameter.</p>
<p>These estimators are still <strong>experimental</strong>: their predictions
and their API might change without any deprecation cycle. To use them, you
need to explicitly import <code class="docutils literal notranslate"><span class="pre">enable_halving_search_cv</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.experimental</span> <span class="kn">import</span> <span class="n">enable_halving_search_cv</span> <span class="c1"># noqa</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">HalvingGridSearchCV</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">HalvingRandomSearchCV</span>
</pre></div>
</div>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p><a class="reference internal" href="../auto_examples/model_selection/plot_successive_halving_heatmap.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-heatmap-py"><span class="std std-ref">Comparison between grid search and successive halving</span></a></p></li>
<li><p><a class="reference internal" href="../auto_examples/model_selection/plot_successive_halving_iterations.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-iterations-py"><span class="std std-ref">Successive Halving Iterations</span></a></p></li>
</ul>
<p>The sections below dive into technical aspects of successive halving.</p>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="choosing-min_resources-and-the-number-of-candidates">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Choosing <code class="docutils literal notranslate"><span class="pre">min_resources</span></code> and the number of candidates<a class="headerlink" href="#choosing-min_resources-and-the-number-of-candidates" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">Beside <code class="docutils literal notranslate"><span class="pre">factor</span></code>, the two main parameters that influence the behaviour of a
successive halving search are the <code class="docutils literal notranslate"><span class="pre">min_resources</span></code> parameter, and the
number of candidates (or parameter combinations) that are evaluated.
<code class="docutils literal notranslate"><span class="pre">min_resources</span></code> is the amount of resources allocated at the first
iteration for each candidate. The number of candidates is specified directly
in <a class="reference internal" href="generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingRandomSearchCV</span></code></a>, and is determined from the <code class="docutils literal notranslate"><span class="pre">param_grid</span></code>
parameter of <a class="reference internal" href="generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a>.</p>
<p class="sd-card-text">Consider a case where the resource is the number of samples, and where we
have 1000 samples. In theory, with <code class="docutils literal notranslate"><span class="pre">min_resources=10</span></code> and <code class="docutils literal notranslate"><span class="pre">factor=2</span></code>, we
are able to run <strong>at most</strong> 7 iterations with the following number of
samples: <code class="docutils literal notranslate"><span class="pre">[10,</span> <span class="pre">20,</span> <span class="pre">40,</span> <span class="pre">80,</span> <span class="pre">160,</span> <span class="pre">320,</span> <span class="pre">640]</span></code>.</p>
<p class="sd-card-text">But depending on the number of candidates, we might run less than 7
iterations: if we start with a <strong>small</strong> number of candidates, the last
iteration might use less than 640 samples, which means not using all the
available resources (samples). For example if we start with 5 candidates, we
only need 2 iterations: 5 candidates for the first iteration, then
<code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">//</span> <span class="pre">2</span> <span class="pre">=</span> <span class="pre">2</span></code> candidates at the second iteration, after which we know which
candidate performs the best (so we don’t need a third one). We would only be
using at most 20 samples which is a waste since we have 1000 samples at our
disposal. On the other hand, if we start with a <strong>high</strong> number of
candidates, we might end up with a lot of candidates at the last iteration,
which may not always be ideal: it means that many candidates will run with
the full resources, basically reducing the procedure to standard search.</p>
<p class="sd-card-text">In the case of <a class="reference internal" href="generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingRandomSearchCV</span></code></a>, the number of candidates is set
by default such that the last iteration uses as much of the available
resources as possible. For <a class="reference internal" href="generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a>, the number of
candidates is determined by the <code class="docutils literal notranslate"><span class="pre">param_grid</span></code> parameter. Changing the value of
<code class="docutils literal notranslate"><span class="pre">min_resources</span></code> will impact the number of possible iterations, and as a
result will also have an effect on the ideal number of candidates.</p>
<p class="sd-card-text">Another consideration when choosing <code class="docutils literal notranslate"><span class="pre">min_resources</span></code> is whether or not it
is easy to discriminate between good and bad candidates with a small amount
of resources. For example, if you need a lot of samples to distinguish
between good and bad parameters, a high <code class="docutils literal notranslate"><span class="pre">min_resources</span></code> is recommended. On
the other hand if the distinction is clear even with a small amount of
samples, then a small <code class="docutils literal notranslate"><span class="pre">min_resources</span></code> may be preferable since it would
speed up the computation.</p>
<p class="sd-card-text">Notice in the example above that the last iteration does not use the maximum
amount of resources available: 1000 samples are available, yet only 640 are
used, at most. By default, both <a class="reference internal" href="generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingRandomSearchCV</span></code></a> and
<a class="reference internal" href="generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a> try to use as many resources as possible in the
last iteration, with the constraint that this amount of resources must be a
multiple of both <code class="docutils literal notranslate"><span class="pre">min_resources</span></code> and <code class="docutils literal notranslate"><span class="pre">factor</span></code> (this constraint will be clear
in the next section). <a class="reference internal" href="generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingRandomSearchCV</span></code></a> achieves this by
sampling the right amount of candidates, while <a class="reference internal" href="generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a>
achieves this by properly setting <code class="docutils literal notranslate"><span class="pre">min_resources</span></code>.</p>
</div>
</details><details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="amount-of-resource-and-number-of-candidates-at-each-iteration">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Amount of resource and number of candidates at each iteration<a class="headerlink" href="#amount-of-resource-and-number-of-candidates-at-each-iteration" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">At any iteration <code class="docutils literal notranslate"><span class="pre">i</span></code>, each candidate is allocated a given amount of resources
which we denote <code class="docutils literal notranslate"><span class="pre">n_resources_i</span></code>. This quantity is controlled by the
parameters <code class="docutils literal notranslate"><span class="pre">factor</span></code> and <code class="docutils literal notranslate"><span class="pre">min_resources</span></code> as follows (<code class="docutils literal notranslate"><span class="pre">factor</span></code> is strictly
greater than 1):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n_resources_i</span> <span class="o">=</span> <span class="n">factor</span><span class="o">**</span><span class="n">i</span> <span class="o">*</span> <span class="n">min_resources</span><span class="p">,</span>
</pre></div>
</div>
<p class="sd-card-text">or equivalently:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n_resources_</span><span class="p">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">}</span> <span class="o">=</span> <span class="n">n_resources_i</span> <span class="o">*</span> <span class="n">factor</span>
</pre></div>
</div>
<p class="sd-card-text">where <code class="docutils literal notranslate"><span class="pre">min_resources</span> <span class="pre">==</span> <span class="pre">n_resources_0</span></code> is the amount of resources used at
the first iteration. <code class="docutils literal notranslate"><span class="pre">factor</span></code> also defines the proportions of candidates
that will be selected for the next iteration:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n_candidates_i</span> <span class="o">=</span> <span class="n">n_candidates</span> <span class="o">//</span> <span class="p">(</span><span class="n">factor</span> <span class="o">**</span> <span class="n">i</span><span class="p">)</span>
</pre></div>
</div>
<p class="sd-card-text">or equivalently:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n_candidates_0</span> <span class="o">=</span> <span class="n">n_candidates</span>
<span class="n">n_candidates_</span><span class="p">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">}</span> <span class="o">=</span> <span class="n">n_candidates_i</span> <span class="o">//</span> <span class="n">factor</span>