-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathv0.13.html
582 lines (549 loc) · 54.8 KB
/
v0.13.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
<!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>Version 0.13.1 — scikit-learn 0.20.4 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: '0.20.4',
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="Version 0.12.1" href="older_versions.html" />
<link rel="prev" title="Version 0.14" href="v0.14.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="https://scikit-learn.org/stable/whats_new/v0.13.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="../modules/classes.html">API</a></li>
<li><a href="../glossary.html">Glossary</a></li>
<li><a href="../faq.html">FAQ</a></li>
<li><a href="../developers/index.html">Development</a></li>
<li><a href="../roadmap.html">Roadmap</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">
<div class="rellink">
<a href="v0.14.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
Version 0.14
</span>
<span class="hiddenrellink">
Version 0.14
</span>
</a>
</div>
<div class="spacer">
</div>
<div class="rellink">
<a href="older_versions.html"
accesskey="N">Next
<br/>
<span class="smallrellink">
Version 0.12.1
</span>
<span class="hiddenrellink">
Version 0.12.1
</span>
</a>
</div>
<!-- Ad a link to the 'up' page -->
<div class="spacer">
</div>
<div class="rellink">
<a href="../whats_new.html">
Up
<br/>
<span class="smallrellink">
Release History
</span>
<span class="hiddenrellink">
Release History
</span>
</a>
</div>
</div>
<p class="doc-version"><b>scikit-learn v0.20.4</b><br/>
<a href="http://scikit-learn.org/dev/versions.html">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="#">Version 0.13.1</a><ul>
<li><a class="reference internal" href="#changelog">Changelog</a></li>
<li><a class="reference internal" href="#people">People</a></li>
</ul>
</li>
<li><a class="reference internal" href="#version-0-13">Version 0.13</a><ul>
<li><a class="reference internal" href="#new-estimator-classes">New Estimator Classes</a></li>
<li><a class="reference internal" href="#id1">Changelog</a></li>
<li><a class="reference internal" href="#api-changes-summary">API changes summary</a></li>
<li><a class="reference internal" href="#id2">People</a></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="version-0-13-1">
<span id="changes-0-13-1"></span><h1>Version 0.13.1<a class="headerlink" href="#version-0-13-1" title="Permalink to this headline">¶</a></h1>
<p><strong>February 23, 2013</strong></p>
<p>The 0.13.1 release only fixes some bugs and does not add any new functionality.</p>
<div class="section" id="changelog">
<h2>Changelog<a class="headerlink" href="#changelog" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Fixed a testing error caused by the function <code class="xref py py-func docutils literal"><span class="pre">cross_validation.train_test_split</span></code> being
interpreted as a test by <a class="reference external" href="http://www.onerussian.com/">Yaroslav Halchenko</a>.</li>
<li>Fixed a bug in the reassignment of small clusters in the <a class="reference internal" href="../modules/generated/sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster.MiniBatchKMeans"><code class="xref py py-class docutils literal"><span class="pre">cluster.MiniBatchKMeans</span></code></a>
by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a>.</li>
<li>Fixed default value of <code class="docutils literal"><span class="pre">gamma</span></code> in <a class="reference internal" href="../modules/generated/sklearn.decomposition.KernelPCA.html#sklearn.decomposition.KernelPCA" title="sklearn.decomposition.KernelPCA"><code class="xref py py-class docutils literal"><span class="pre">decomposition.KernelPCA</span></code></a> by <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a>.</li>
<li>Updated joblib to <code class="docutils literal"><span class="pre">0.7.0d</span></code> by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a>.</li>
<li>Fixed scaling of the deviance in <a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-class docutils literal"><span class="pre">ensemble.GradientBoostingClassifier</span></code></a> by <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a>.</li>
<li>Better tie-breaking in <a class="reference internal" href="../modules/generated/sklearn.multiclass.OneVsOneClassifier.html#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><code class="xref py py-class docutils literal"><span class="pre">multiclass.OneVsOneClassifier</span></code></a> by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</li>
<li>Other small improvements to tests and documentation.</li>
</ul>
</div>
<div class="section" id="people">
<h2>People<a class="headerlink" href="#people" title="Permalink to this headline">¶</a></h2>
<dl class="docutils">
<dt>List of contributors for release 0.13.1 by number of commits.</dt>
<dd><ul class="first last simple">
<li>16 <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a></li>
<li>12 <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a></li>
<li>8 <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a></li>
<li>5 Robert Marchman</li>
<li>3 <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a></li>
<li>2 Hrishikesh Huilgolkar</li>
<li>1 Bastiaan van den Berg</li>
<li>1 Diego Molla</li>
<li>1 <a class="reference external" href="http://www.montefiore.ulg.ac.be/~glouppe/">Gilles Louppe</a></li>
<li>1 <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a></li>
<li>1 <a class="reference external" href="https://github.com/nellev">Nelle Varoquaux</a></li>
<li>1 Rafael Cunha de Almeida</li>
<li>1 Rolando Espinoza La fuente</li>
<li>1 <a class="reference external" href="https://vene.ro/">Vlad Niculae</a></li>
<li>1 <a class="reference external" href="http://www.onerussian.com/">Yaroslav Halchenko</a></li>
</ul>
</dd>
</dl>
</div>
</div>
<div class="section" id="version-0-13">
<span id="changes-0-13"></span><h1>Version 0.13<a class="headerlink" href="#version-0-13" title="Permalink to this headline">¶</a></h1>
<p><strong>January 21, 2013</strong></p>
<div class="section" id="new-estimator-classes">
<h2>New Estimator Classes<a class="headerlink" href="#new-estimator-classes" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="../modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier"><code class="xref py py-class docutils literal"><span class="pre">dummy.DummyClassifier</span></code></a> and <a class="reference internal" href="../modules/generated/sklearn.dummy.DummyRegressor.html#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><code class="xref py py-class docutils literal"><span class="pre">dummy.DummyRegressor</span></code></a>, two
data-independent predictors by <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a>. Useful to sanity-check
your estimators. See <a class="reference internal" href="../modules/model_evaluation.html#dummy-estimators"><span class="std std-ref">Dummy estimators</span></a> in the user guide.
Multioutput support added by <a class="reference external" href="http://www.ajoly.org">Arnaud Joly</a>.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.decomposition.FactorAnalysis.html#sklearn.decomposition.FactorAnalysis" title="sklearn.decomposition.FactorAnalysis"><code class="xref py py-class docutils literal"><span class="pre">decomposition.FactorAnalysis</span></code></a>, a transformer implementing the
classical factor analysis, by <a class="reference external" href="https://osdf.github.io">Christian Osendorfer</a> and <a class="reference external" href="http://alexandre.gramfort.net">Alexandre
Gramfort</a>. See <a class="reference internal" href="../modules/decomposition.html#fa"><span class="std std-ref">Factor Analysis</span></a> in the user guide.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher" title="sklearn.feature_extraction.FeatureHasher"><code class="xref py py-class docutils literal"><span class="pre">feature_extraction.FeatureHasher</span></code></a>, a transformer implementing the
“hashing trick” for fast, low-memory feature extraction from string fields
by <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a> and <a class="reference internal" href="../modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html#sklearn.feature_extraction.text.HashingVectorizer" title="sklearn.feature_extraction.text.HashingVectorizer"><code class="xref py py-class docutils literal"><span class="pre">feature_extraction.text.HashingVectorizer</span></code></a>
for text documents by <a class="reference external" href="https://twitter.com/ogrisel">Olivier Grisel</a> See <a class="reference internal" href="../modules/feature_extraction.html#feature-hashing"><span class="std std-ref">Feature hashing</span></a> and
<a class="reference internal" href="../modules/feature_extraction.html#hashing-vectorizer"><span class="std std-ref">Vectorizing a large text corpus with the hashing trick</span></a> for the documentation and sample usage.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-class docutils literal"><span class="pre">pipeline.FeatureUnion</span></code></a>, a transformer that concatenates
results of several other transformers by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>. See
<a class="reference internal" href="../modules/compose.html#feature-union"><span class="std std-ref">FeatureUnion: composite feature spaces</span></a> in the user guide.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.random_projection.GaussianRandomProjection.html#sklearn.random_projection.GaussianRandomProjection" title="sklearn.random_projection.GaussianRandomProjection"><code class="xref py py-class docutils literal"><span class="pre">random_projection.GaussianRandomProjection</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.random_projection.SparseRandomProjection.html#sklearn.random_projection.SparseRandomProjection" title="sklearn.random_projection.SparseRandomProjection"><code class="xref py py-class docutils literal"><span class="pre">random_projection.SparseRandomProjection</span></code></a> and the function
<a class="reference internal" href="../modules/generated/sklearn.random_projection.johnson_lindenstrauss_min_dim.html#sklearn.random_projection.johnson_lindenstrauss_min_dim" title="sklearn.random_projection.johnson_lindenstrauss_min_dim"><code class="xref py py-func docutils literal"><span class="pre">random_projection.johnson_lindenstrauss_min_dim</span></code></a>. The first two are
transformers implementing Gaussian and sparse random projection matrix
by <a class="reference external" href="https://twitter.com/ogrisel">Olivier Grisel</a> and <a class="reference external" href="http://www.ajoly.org">Arnaud Joly</a>.
See <a class="reference internal" href="../modules/random_projection.html#random-projection"><span class="std std-ref">Random Projection</span></a> in the user guide.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.kernel_approximation.Nystroem.html#sklearn.kernel_approximation.Nystroem" title="sklearn.kernel_approximation.Nystroem"><code class="xref py py-class docutils literal"><span class="pre">kernel_approximation.Nystroem</span></code></a>, a transformer for approximating
arbitrary kernels by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>. See
<a class="reference internal" href="../modules/kernel_approximation.html#nystroem-kernel-approx"><span class="std std-ref">Nystroem Method for Kernel Approximation</span></a> in the user guide.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder"><code class="xref py py-class docutils literal"><span class="pre">preprocessing.OneHotEncoder</span></code></a>, a transformer that computes binary
encodings of categorical features by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>. See
<a class="reference internal" href="../modules/preprocessing.html#preprocessing-categorical-features"><span class="std std-ref">Encoding categorical features</span></a> in the user guide.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model.PassiveAggressiveClassifier"><code class="xref py py-class docutils literal"><span class="pre">linear_model.PassiveAggressiveClassifier</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model.PassiveAggressiveRegressor"><code class="xref py py-class docutils literal"><span class="pre">linear_model.PassiveAggressiveRegressor</span></code></a>, predictors implementing
an efficient stochastic optimization for linear models by <a class="reference external" href="https://www.zinkov.com/">Rob Zinkov</a> and
<a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a>. See <a class="reference internal" href="../modules/linear_model.html#passive-aggressive"><span class="std std-ref">Passive Aggressive Algorithms</span></a> in the user
guide.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble.RandomTreesEmbedding"><code class="xref py py-class docutils literal"><span class="pre">ensemble.RandomTreesEmbedding</span></code></a>, a transformer for creating high-dimensional
sparse representations using ensembles of totally random trees by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.
See <a class="reference internal" href="../modules/ensemble.html#random-trees-embedding"><span class="std std-ref">Totally Random Trees Embedding</span></a> in the user guide.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.manifold.SpectralEmbedding.html#sklearn.manifold.SpectralEmbedding" title="sklearn.manifold.SpectralEmbedding"><code class="xref py py-class docutils literal"><span class="pre">manifold.SpectralEmbedding</span></code></a> and function
<a class="reference internal" href="../modules/generated/sklearn.manifold.spectral_embedding.html#sklearn.manifold.spectral_embedding" title="sklearn.manifold.spectral_embedding"><code class="xref py py-func docutils literal"><span class="pre">manifold.spectral_embedding</span></code></a>, implementing the “laplacian
eigenmaps” transformation for non-linear dimensionality reduction by Wei
Li. See <a class="reference internal" href="../modules/manifold.html#spectral-embedding"><span class="std std-ref">Spectral Embedding</span></a> in the user guide.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.isotonic.IsotonicRegression.html#sklearn.isotonic.IsotonicRegression" title="sklearn.isotonic.IsotonicRegression"><code class="xref py py-class docutils literal"><span class="pre">isotonic.IsotonicRegression</span></code></a> by <a class="reference external" href="http://fa.bianp.net">Fabian Pedregosa</a>, <a class="reference external" href="http://alexandre.gramfort.net">Alexandre Gramfort</a>
and <a class="reference external" href="https://github.com/nellev">Nelle Varoquaux</a>,</li>
</ul>
</div>
<div class="section" id="id1">
<h2>Changelog<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="../modules/generated/sklearn.metrics.zero_one_loss.html#sklearn.metrics.zero_one_loss" title="sklearn.metrics.zero_one_loss"><code class="xref py py-func docutils literal"><span class="pre">metrics.zero_one_loss</span></code></a> (formerly <code class="docutils literal"><span class="pre">metrics.zero_one</span></code>) now has
option for normalized output that reports the fraction of
misclassifications, rather than the raw number of misclassifications. By
Kyle Beauchamp.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-class docutils literal"><span class="pre">tree.DecisionTreeClassifier</span></code></a> and all derived ensemble models now
support sample weighting, by <a class="reference external" href="https://github.com/ndawe">Noel Dawe</a> and <a class="reference external" href="http://www.montefiore.ulg.ac.be/~glouppe/">Gilles Louppe</a>.</li>
<li>Speedup improvement when using bootstrap samples in forests of randomized
trees, by <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a> and <a class="reference external" href="http://www.montefiore.ulg.ac.be/~glouppe/">Gilles Louppe</a>.</li>
<li>Partial dependence plots for <a class="reference internal" href="../modules/ensemble.html#gradient-boosting"><span class="std std-ref">Gradient Tree Boosting</span></a> in
<a class="reference internal" href="../modules/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-func docutils literal"><span class="pre">ensemble.partial_dependence.partial_dependence</span></code></a> by <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter
Prettenhofer</a>. See <a class="reference internal" href="../auto_examples/ensemble/plot_partial_dependence.html#sphx-glr-auto-examples-ensemble-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence Plots</span></a> for an
example.</li>
<li>The table of contents on the website has now been made expandable by
<a class="reference external" href="https://github.com/jaquesgrobler">Jaques Grobler</a>.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.feature_selection.SelectPercentile.html#sklearn.feature_selection.SelectPercentile" title="sklearn.feature_selection.SelectPercentile"><code class="xref py py-class docutils literal"><span class="pre">feature_selection.SelectPercentile</span></code></a> now breaks ties
deterministically instead of returning all equally ranked features.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest"><code class="xref py py-class docutils literal"><span class="pre">feature_selection.SelectKBest</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.feature_selection.SelectPercentile.html#sklearn.feature_selection.SelectPercentile" title="sklearn.feature_selection.SelectPercentile"><code class="xref py py-class docutils literal"><span class="pre">feature_selection.SelectPercentile</span></code></a> are more numerically stable
since they use scores, rather than p-values, to rank results. This means
that they might sometimes select different features than they did
previously.</li>
<li>Ridge regression and ridge classification fitting with <code class="docutils literal"><span class="pre">sparse_cg</span></code> solver
no longer has quadratic memory complexity, by <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a> and
<a class="reference external" href="http://fa.bianp.net">Fabian Pedregosa</a>.</li>
<li>Ridge regression and ridge classification now support a new fast solver
called <code class="docutils literal"><span class="pre">lsqr</span></code>, by <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a>.</li>
<li>Speed up of <a class="reference internal" href="../modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve" title="sklearn.metrics.precision_recall_curve"><code class="xref py py-func docutils literal"><span class="pre">metrics.precision_recall_curve</span></code></a> by Conrad Lee.</li>
<li>Added support for reading/writing svmlight files with pairwise
preference attribute (qid in svmlight file format) in
<a class="reference internal" href="../modules/generated/sklearn.datasets.dump_svmlight_file.html#sklearn.datasets.dump_svmlight_file" title="sklearn.datasets.dump_svmlight_file"><code class="xref py py-func docutils literal"><span class="pre">datasets.dump_svmlight_file</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.datasets.load_svmlight_file.html#sklearn.datasets.load_svmlight_file" title="sklearn.datasets.load_svmlight_file"><code class="xref py py-func docutils literal"><span class="pre">datasets.load_svmlight_file</span></code></a> by <a class="reference external" href="http://fa.bianp.net">Fabian Pedregosa</a>.</li>
<li>Faster and more robust <a class="reference internal" href="../modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix"><code class="xref py py-func docutils literal"><span class="pre">metrics.confusion_matrix</span></code></a> and
<a class="reference internal" href="../modules/clustering.html#clustering-evaluation"><span class="std std-ref">Clustering performance evaluation</span></a> by Wei Li.</li>
<li><code class="xref py py-func docutils literal"><span class="pre">cross_validation.cross_val_score</span></code> now works with precomputed kernels
and affinity matrices, by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</li>
<li>LARS algorithm made more numerically stable with heuristics to drop
regressors too correlated as well as to stop the path when
numerical noise becomes predominant, by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a>.</li>
<li>Faster implementation of <a class="reference internal" href="../modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve" title="sklearn.metrics.precision_recall_curve"><code class="xref py py-func docutils literal"><span class="pre">metrics.precision_recall_curve</span></code></a> by
Conrad Lee.</li>
<li>New kernel <code class="xref py py-class docutils literal"><span class="pre">metrics.chi2_kernel</span></code> by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>, often used
in computer vision applications.</li>
<li>Fix of longstanding bug in <a class="reference internal" href="../modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB" title="sklearn.naive_bayes.BernoulliNB"><code class="xref py py-class docutils literal"><span class="pre">naive_bayes.BernoulliNB</span></code></a> fixed by
Shaun Jackman.</li>
<li>Implemented <code class="docutils literal"><span class="pre">predict_proba</span></code> in <a class="reference internal" href="../modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><code class="xref py py-class docutils literal"><span class="pre">multiclass.OneVsRestClassifier</span></code></a>,
by Andrew Winterman.</li>
<li>Improve consistency in gradient boosting: estimators
<a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-class docutils literal"><span class="pre">ensemble.GradientBoostingRegressor</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-class docutils literal"><span class="pre">ensemble.GradientBoostingClassifier</span></code></a> use the estimator
<a class="reference internal" href="../modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree.DecisionTreeRegressor"><code class="xref py py-class docutils literal"><span class="pre">tree.DecisionTreeRegressor</span></code></a> instead of the
<code class="xref py py-class docutils literal"><span class="pre">tree._tree.Tree</span></code> data structure by <a class="reference external" href="http://www.ajoly.org">Arnaud Joly</a>.</li>
<li>Fixed a floating point exception in the <a class="reference internal" href="../modules/tree.html#tree"><span class="std std-ref">decision trees</span></a>
module, by Seberg.</li>
<li>Fix <a class="reference internal" href="../modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve" title="sklearn.metrics.roc_curve"><code class="xref py py-func docutils literal"><span class="pre">metrics.roc_curve</span></code></a> fails when y_true has only one class
by Wei Li.</li>
<li>Add the <a class="reference internal" href="../modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error" title="sklearn.metrics.mean_absolute_error"><code class="xref py py-func docutils literal"><span class="pre">metrics.mean_absolute_error</span></code></a> function which computes the
mean absolute error. The <a class="reference internal" href="../modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error" title="sklearn.metrics.mean_squared_error"><code class="xref py py-func docutils literal"><span class="pre">metrics.mean_squared_error</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error" title="sklearn.metrics.mean_absolute_error"><code class="xref py py-func docutils literal"><span class="pre">metrics.mean_absolute_error</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal"><span class="pre">metrics.r2_score</span></code></a> metrics support multioutput by <a class="reference external" href="http://www.ajoly.org">Arnaud Joly</a>.</li>
<li>Fixed <code class="docutils literal"><span class="pre">class_weight</span></code> support in <a class="reference internal" href="../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC"><code class="xref py py-class docutils literal"><span class="pre">svm.LinearSVC</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal"><span class="pre">linear_model.LogisticRegression</span></code></a> by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>. The meaning
of <code class="docutils literal"><span class="pre">class_weight</span></code> was reversed as erroneously higher weight meant less
positives of a given class in earlier releases.</li>
<li>Improve narrative documentation and consistency in
<a class="reference internal" href="../modules/classes.html#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.metrics</span></code></a> for regression and classification metrics
by <a class="reference external" href="http://www.ajoly.org">Arnaud Joly</a>.</li>
<li>Fixed a bug in <a class="reference internal" href="../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC"><code class="xref py py-class docutils literal"><span class="pre">sklearn.svm.SVC</span></code></a> when using csr-matrices with
unsorted indices by Xinfan Meng and <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</li>
<li><code class="xref py py-class docutils literal"><span class="pre">MiniBatchKMeans</span></code>: Add random reassignment of cluster centers
with little observations attached to them, by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a>.</li>
</ul>
</div>
<div class="section" id="api-changes-summary">
<h2>API changes summary<a class="headerlink" href="#api-changes-summary" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Renamed all occurrences of <code class="docutils literal"><span class="pre">n_atoms</span></code> to <code class="docutils literal"><span class="pre">n_components</span></code> for consistency.
This applies to <a class="reference internal" href="../modules/generated/sklearn.decomposition.DictionaryLearning.html#sklearn.decomposition.DictionaryLearning" title="sklearn.decomposition.DictionaryLearning"><code class="xref py py-class docutils literal"><span class="pre">decomposition.DictionaryLearning</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html#sklearn.decomposition.MiniBatchDictionaryLearning" title="sklearn.decomposition.MiniBatchDictionaryLearning"><code class="xref py py-class docutils literal"><span class="pre">decomposition.MiniBatchDictionaryLearning</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.decomposition.dict_learning.html#sklearn.decomposition.dict_learning" title="sklearn.decomposition.dict_learning"><code class="xref py py-func docutils literal"><span class="pre">decomposition.dict_learning</span></code></a>, <a class="reference internal" href="../modules/generated/sklearn.decomposition.dict_learning_online.html#sklearn.decomposition.dict_learning_online" title="sklearn.decomposition.dict_learning_online"><code class="xref py py-func docutils literal"><span class="pre">decomposition.dict_learning_online</span></code></a>.</li>
<li>Renamed all occurrences of <code class="docutils literal"><span class="pre">max_iters</span></code> to <code class="docutils literal"><span class="pre">max_iter</span></code> for consistency.
This applies to <a class="reference internal" href="../modules/generated/sklearn.semi_supervised.LabelPropagation.html#sklearn.semi_supervised.LabelPropagation" title="sklearn.semi_supervised.LabelPropagation"><code class="xref py py-class docutils literal"><span class="pre">semi_supervised.LabelPropagation</span></code></a> and
<code class="xref py py-class docutils literal"><span class="pre">semi_supervised.label_propagation.LabelSpreading</span></code>.</li>
<li>Renamed all occurrences of <code class="docutils literal"><span class="pre">learn_rate</span></code> to <code class="docutils literal"><span class="pre">learning_rate</span></code> for
consistency in <code class="xref py py-class docutils literal"><span class="pre">ensemble.BaseGradientBoosting</span></code> and
<a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-class docutils literal"><span class="pre">ensemble.GradientBoostingRegressor</span></code></a>.</li>
<li>The module <code class="docutils literal"><span class="pre">sklearn.linear_model.sparse</span></code> is gone. Sparse matrix support
was already integrated into the “regular” linear models.</li>
<li><code class="xref py py-func docutils literal"><span class="pre">sklearn.metrics.mean_square_error</span></code>, which incorrectly returned the
accumulated error, was removed. Use <code class="docutils literal"><span class="pre">mean_squared_error</span></code> instead.</li>
<li>Passing <code class="docutils literal"><span class="pre">class_weight</span></code> parameters to <code class="docutils literal"><span class="pre">fit</span></code> methods is no longer
supported. Pass them to estimator constructors instead.</li>
<li>GMMs no longer have <code class="docutils literal"><span class="pre">decode</span></code> and <code class="docutils literal"><span class="pre">rvs</span></code> methods. Use the <code class="docutils literal"><span class="pre">score</span></code>,
<code class="docutils literal"><span class="pre">predict</span></code> or <code class="docutils literal"><span class="pre">sample</span></code> methods instead.</li>
<li>The <code class="docutils literal"><span class="pre">solver</span></code> fit option in Ridge regression and classification is now
deprecated and will be removed in v0.14. Use the constructor option
instead.</li>
<li><code class="xref py py-class docutils literal"><span class="pre">feature_extraction.text.DictVectorizer</span></code> now returns sparse
matrices in the CSR format, instead of COO.</li>
<li>Renamed <code class="docutils literal"><span class="pre">k</span></code> in <code class="xref py py-class docutils literal"><span class="pre">cross_validation.KFold</span></code> and
<code class="xref py py-class docutils literal"><span class="pre">cross_validation.StratifiedKFold</span></code> to <code class="docutils literal"><span class="pre">n_folds</span></code>, renamed
<code class="docutils literal"><span class="pre">n_bootstraps</span></code> to <code class="docutils literal"><span class="pre">n_iter</span></code> in <code class="docutils literal"><span class="pre">cross_validation.Bootstrap</span></code>.</li>
<li>Renamed all occurrences of <code class="docutils literal"><span class="pre">n_iterations</span></code> to <code class="docutils literal"><span class="pre">n_iter</span></code> for consistency.
This applies to <code class="xref py py-class docutils literal"><span class="pre">cross_validation.ShuffleSplit</span></code>,
<code class="xref py py-class docutils literal"><span class="pre">cross_validation.StratifiedShuffleSplit</span></code>,
<code class="xref py py-func docutils literal"><span class="pre">utils.randomized_range_finder</span></code> and <code class="xref py py-func docutils literal"><span class="pre">utils.randomized_svd</span></code>.</li>
<li>Replaced <code class="docutils literal"><span class="pre">rho</span></code> in <a class="reference internal" href="../modules/generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet" title="sklearn.linear_model.ElasticNet"><code class="xref py py-class docutils literal"><span class="pre">linear_model.ElasticNet</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-class docutils literal"><span class="pre">linear_model.SGDClassifier</span></code></a> by <code class="docutils literal"><span class="pre">l1_ratio</span></code>. The <code class="docutils literal"><span class="pre">rho</span></code> parameter
had different meanings; <code class="docutils literal"><span class="pre">l1_ratio</span></code> was introduced to avoid confusion.
It has the same meaning as previously <code class="docutils literal"><span class="pre">rho</span></code> in
<a class="reference internal" href="../modules/generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet" title="sklearn.linear_model.ElasticNet"><code class="xref py py-class docutils literal"><span class="pre">linear_model.ElasticNet</span></code></a> and <code class="docutils literal"><span class="pre">(1-rho)</span></code> in
<a class="reference internal" href="../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-class docutils literal"><span class="pre">linear_model.SGDClassifier</span></code></a>.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.linear_model.LassoLars.html#sklearn.linear_model.LassoLars" title="sklearn.linear_model.LassoLars"><code class="xref py py-class docutils literal"><span class="pre">linear_model.LassoLars</span></code></a> and <a class="reference internal" href="../modules/generated/sklearn.linear_model.Lars.html#sklearn.linear_model.Lars" title="sklearn.linear_model.Lars"><code class="xref py py-class docutils literal"><span class="pre">linear_model.Lars</span></code></a> now
store a list of paths in the case of multiple targets, rather than
an array of paths.</li>
<li>The attribute <code class="docutils literal"><span class="pre">gmm</span></code> of <code class="xref py py-class docutils literal"><span class="pre">hmm.GMMHMM</span></code> was renamed to <code class="docutils literal"><span class="pre">gmm_</span></code>
to adhere more strictly with the API.</li>
<li><code class="xref py py-func docutils literal"><span class="pre">cluster.spectral_embedding</span></code> was moved to
<a class="reference internal" href="../modules/generated/sklearn.manifold.spectral_embedding.html#sklearn.manifold.spectral_embedding" title="sklearn.manifold.spectral_embedding"><code class="xref py py-func docutils literal"><span class="pre">manifold.spectral_embedding</span></code></a>.</li>
<li>Renamed <code class="docutils literal"><span class="pre">eig_tol</span></code> in <a class="reference internal" href="../modules/generated/sklearn.manifold.spectral_embedding.html#sklearn.manifold.spectral_embedding" title="sklearn.manifold.spectral_embedding"><code class="xref py py-func docutils literal"><span class="pre">manifold.spectral_embedding</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering" title="sklearn.cluster.SpectralClustering"><code class="xref py py-class docutils literal"><span class="pre">cluster.SpectralClustering</span></code></a> to <code class="docutils literal"><span class="pre">eigen_tol</span></code>, renamed <code class="docutils literal"><span class="pre">mode</span></code>
to <code class="docutils literal"><span class="pre">eigen_solver</span></code>.</li>
<li>Renamed <code class="docutils literal"><span class="pre">mode</span></code> in <a class="reference internal" href="../modules/generated/sklearn.manifold.spectral_embedding.html#sklearn.manifold.spectral_embedding" title="sklearn.manifold.spectral_embedding"><code class="xref py py-func docutils literal"><span class="pre">manifold.spectral_embedding</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering" title="sklearn.cluster.SpectralClustering"><code class="xref py py-class docutils literal"><span class="pre">cluster.SpectralClustering</span></code></a> to <code class="docutils literal"><span class="pre">eigen_solver</span></code>.</li>
<li><code class="docutils literal"><span class="pre">classes_</span></code> and <code class="docutils literal"><span class="pre">n_classes_</span></code> attributes of
<a class="reference internal" href="../modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-class docutils literal"><span class="pre">tree.DecisionTreeClassifier</span></code></a> and all derived ensemble models are
now flat in case of single output problems and nested in case of
multi-output problems.</li>
<li>The <code class="docutils literal"><span class="pre">estimators_</span></code> attribute of
<code class="xref py py-class docutils literal"><span class="pre">ensemble.gradient_boosting.GradientBoostingRegressor</span></code> and
<code class="xref py py-class docutils literal"><span class="pre">ensemble.gradient_boosting.GradientBoostingClassifier</span></code> is now an
array of :class:’tree.DecisionTreeRegressor’.</li>
<li>Renamed <code class="docutils literal"><span class="pre">chunk_size</span></code> to <code class="docutils literal"><span class="pre">batch_size</span></code> in
<a class="reference internal" href="../modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html#sklearn.decomposition.MiniBatchDictionaryLearning" title="sklearn.decomposition.MiniBatchDictionaryLearning"><code class="xref py py-class docutils literal"><span class="pre">decomposition.MiniBatchDictionaryLearning</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.decomposition.MiniBatchSparsePCA.html#sklearn.decomposition.MiniBatchSparsePCA" title="sklearn.decomposition.MiniBatchSparsePCA"><code class="xref py py-class docutils literal"><span class="pre">decomposition.MiniBatchSparsePCA</span></code></a> for consistency.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC"><code class="xref py py-class docutils literal"><span class="pre">svm.SVC</span></code></a> and <a class="reference internal" href="../modules/generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC" title="sklearn.svm.NuSVC"><code class="xref py py-class docutils literal"><span class="pre">svm.NuSVC</span></code></a> now provide a <code class="docutils literal"><span class="pre">classes_</span></code>
attribute and support arbitrary dtypes for labels <code class="docutils literal"><span class="pre">y</span></code>.
Also, the dtype returned by <code class="docutils literal"><span class="pre">predict</span></code> now reflects the dtype of
<code class="docutils literal"><span class="pre">y</span></code> during <code class="docutils literal"><span class="pre">fit</span></code> (used to be <code class="docutils literal"><span class="pre">np.float</span></code>).</li>
<li>Changed default test_size in <code class="xref py py-func docutils literal"><span class="pre">cross_validation.train_test_split</span></code>
to None, added possibility to infer <code class="docutils literal"><span class="pre">test_size</span></code> from <code class="docutils literal"><span class="pre">train_size</span></code> in
<code class="xref py py-class docutils literal"><span class="pre">cross_validation.ShuffleSplit</span></code> and
<code class="xref py py-class docutils literal"><span class="pre">cross_validation.StratifiedShuffleSplit</span></code>.</li>
<li>Renamed function <code class="xref py py-func docutils literal"><span class="pre">sklearn.metrics.zero_one</span></code> to
<a class="reference internal" href="../modules/generated/sklearn.metrics.zero_one_loss.html#sklearn.metrics.zero_one_loss" title="sklearn.metrics.zero_one_loss"><code class="xref py py-func docutils literal"><span class="pre">sklearn.metrics.zero_one_loss</span></code></a>. Be aware that the default behavior
in <a class="reference internal" href="../modules/generated/sklearn.metrics.zero_one_loss.html#sklearn.metrics.zero_one_loss" title="sklearn.metrics.zero_one_loss"><code class="xref py py-func docutils literal"><span class="pre">sklearn.metrics.zero_one_loss</span></code></a> is different from
<code class="xref py py-func docutils literal"><span class="pre">sklearn.metrics.zero_one</span></code>: <code class="docutils literal"><span class="pre">normalize=False</span></code> is changed to
<code class="docutils literal"><span class="pre">normalize=True</span></code>.</li>
<li>Renamed function <code class="xref py py-func docutils literal"><span class="pre">metrics.zero_one_score</span></code> to
<a class="reference internal" href="../modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score" title="sklearn.metrics.accuracy_score"><code class="xref py py-func docutils literal"><span class="pre">metrics.accuracy_score</span></code></a>.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles"><code class="xref py py-func docutils literal"><span class="pre">datasets.make_circles</span></code></a> now has the same number of inner and outer points.</li>
<li>In the Naive Bayes classifiers, the <code class="docutils literal"><span class="pre">class_prior</span></code> parameter was moved
from <code class="docutils literal"><span class="pre">fit</span></code> to <code class="docutils literal"><span class="pre">__init__</span></code>.</li>
</ul>
</div>
<div class="section" id="id2">
<h2>People<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
<p>List of contributors for release 0.13 by number of commits.</p>
<blockquote>
<div><ul class="simple">
<li>364 <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a></li>
<li>143 <a class="reference external" href="http://www.ajoly.org">Arnaud Joly</a></li>
<li>137 <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a></li>
<li>131 <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a></li>
<li>117 <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a></li>
<li>108 <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a></li>
<li>106 Wei Li</li>
<li>101 <a class="reference external" href="https://twitter.com/ogrisel">Olivier Grisel</a></li>
<li>65 <a class="reference external" href="https://vene.ro/">Vlad Niculae</a></li>
<li>54 <a class="reference external" href="http://www.montefiore.ulg.ac.be/~glouppe/">Gilles Louppe</a></li>
<li>40 <a class="reference external" href="https://github.com/jaquesgrobler">Jaques Grobler</a></li>
<li>38 <a class="reference external" href="http://alexandre.gramfort.net">Alexandre Gramfort</a></li>
<li>30 <a class="reference external" href="https://www.zinkov.com/">Rob Zinkov</a></li>
<li>19 Aymeric Masurelle</li>
<li>18 Andrew Winterman</li>
<li>17 <a class="reference external" href="http://fa.bianp.net">Fabian Pedregosa</a></li>
<li>17 Nelle Varoquaux</li>
<li>16 <a class="reference external" href="https://osdf.github.io">Christian Osendorfer</a></li>
<li>14 <a class="reference external" href="http://danielnouri.org">Daniel Nouri</a></li>
<li>13 <a class="reference external" href="https://github.com/VirgileFritsch">Virgile Fritsch</a></li>
<li>13 syhw</li>
<li>12 <a class="reference external" href="http://www.mit.edu/~satra/">Satrajit Ghosh</a></li>
<li>10 Corey Lynch</li>
<li>10 Kyle Beauchamp</li>
<li>9 Brian Cheung</li>
<li>9 Immanuel Bayer</li>
<li>9 mr.Shu</li>
<li>8 Conrad Lee</li>
<li>8 <a class="reference external" href="http://www-etud.iro.umontreal.ca/~bergstrj/">James Bergstra</a></li>
<li>7 Tadej Janež</li>
<li>6 Brian Cajes</li>
<li>6 <a class="reference external" href="http://staff.washington.edu/jakevdp/">Jake Vanderplas</a></li>
<li>6 Michael</li>
<li>6 Noel Dawe</li>
<li>6 Tiago Nunes</li>
<li>6 cow</li>
<li>5 Anze</li>
<li>5 Shiqiao Du</li>
<li>4 Christian Jauvin</li>
<li>4 Jacques Kvam</li>
<li>4 Richard T. Guy</li>
<li>4 <a class="reference external" href="https://twitter.com/robertlayton">Robert Layton</a></li>
<li>3 Alexandre Abraham</li>
<li>3 Doug Coleman</li>
<li>3 Scott Dickerson</li>
<li>2 ApproximateIdentity</li>
<li>2 John Benediktsson</li>
<li>2 Mark Veronda</li>
<li>2 Matti Lyra</li>
<li>2 Mikhail Korobov</li>
<li>2 Xinfan Meng</li>
<li>1 Alejandro Weinstein</li>
<li>1 <a class="reference external" href="http://atpassos.me">Alexandre Passos</a></li>
<li>1 Christoph Deil</li>
<li>1 Eugene Nizhibitsky</li>
<li>1 Kenneth C. Arnold</li>
<li>1 Luis Pedro Coelho</li>
<li>1 Miroslav Batchkarov</li>
<li>1 Pavel</li>
<li>1 Sebastian Berg</li>
<li>1 Shaun Jackman</li>
<li>1 Subhodeep Moitra</li>
<li>1 bob</li>
<li>1 dengemann</li>
<li>1 emanuele</li>
<li>1 x006</li>
</ul>
</div></blockquote>
</div>
</div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
<div class="footer">
© 2007 - 2018, scikit-learn developers (BSD License).
<a href="../_sources/whats_new/v0.13.rst.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="v0.14.html">Previous
</a>
</div>
<div class="buttonNext">
<a href="older_versions.html">Next
</a>
</div>
</div>
<script>
window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
ga('create', 'UA-22606712-2', 'auto');
ga('set', 'anonymizeIp', true);
ga('send', 'pageview');
</script>
<script async src='https://www.google-analytics.com/analytics.js'></script>
<script>
(function() {
var cx = '016639176250731907682:tjtqbvtvij0';
var gcse = document.createElement('script'); gcse.type = 'text/javascript'; gcse.async = true;
gcse.src = 'https://cse.google.com/cse.js?cx=' + cx;
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(gcse, s);
})();
</script>
<script src="https://scikit-learn.org/versionwarning.js"></script>
</body>
</html>