-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathsklearn.cross_validation.KFold.html
311 lines (270 loc) · 17.9 KB
/
sklearn.cross_validation.KFold.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
<!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>sklearn.cross_validation.KFold — scikit-learn 0.17.1 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: '0.17.1',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true
};
</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>
<link rel="shortcut icon" href="../../_static/favicon.ico"/>
<link rel="author" title="About these documents" href="../../about.html" />
<link rel="top" title="scikit-learn 0.17.1 documentation" href="../../index.html" />
<link rel="up" title="API Reference" href="../classes.html" />
<link rel="next" title="sklearn.cross_validation.LabelKFold" href="sklearn.cross_validation.LabelKFold.html" />
<link rel="prev" title="sklearn.covariance.graph_lasso" href="sklearn.covariance.graph_lasso.html" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script src="../../_static/js/bootstrap.min.js" type="text/javascript"></script>
<link rel="canonical" href="https://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.KFold.html" />
<script type="text/javascript">
$("div.buttonNext, div.buttonPrevious").hover(
function () {
$(this).css('background-color', '#FF9C34');
},
function () {
$(this).css('background-color', '#A7D6E2');
}
);
</script>
</head>
<body role="document">
<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">
<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 0.17 (stable)</li>
<li><a href="../../tutorial/index.html">Tutorials</a></li>
<li><a href="../../user_guide.html">User guide</a></li>
<li><a href="../classes.html">API</a></li>
<li><a href="../../faq.html">FAQ</a></li>
<li><a href="../../developers/contributing.html">Contributing</a></li>
<li class="divider"></li>
<li><a href="http://scikit-learn.org/dev/documentation.html">Scikit-learn 0.18 (development)</a></li>
<li><a href="http://scikit-learn.org/0.16/documentation.html">Scikit-learn 0.16</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>
<div class="search_form">
<div 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">
<!-- rellinks[1:] is an ugly hack to avoid link to module
index -->
<div class="rellink">
<a href="sklearn.covariance.graph_lasso.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
sklearn.covar...
</span>
<span class="hiddenrellink">
sklearn.covariance.graph_lasso
</span>
</a>
</div>
<!-- Ad a link to the 'up' page -->
<div class="spacer">
</div>
<div class="rellink">
<a href="../classes.html">
Up
<br/>
<span class="smallrellink">
API Reference
</span>
<span class="hiddenrellink">
API Reference
</span>
</a>
</div>
</div>
<p class="doc-version">This documentation is for scikit-learn <strong>version 0.17.1</strong> — <a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a></p>
<p class="citing">If you use the software, please consider <a href="../../about.html#citing-scikit-learn">citing scikit-learn</a>.</p>
<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cross_validation</span></code>.KFold</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cross-validation-kfold">Examples using <code class="docutils literal"><span class="pre">sklearn.cross_validation.KFold</span></code></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="sklearn-cross-validation-kfold">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cross_validation" title="sklearn.cross_validation"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cross_validation</span></code></a>.KFold<a class="headerlink" href="#sklearn-cross-validation-kfold" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cross_validation.KFold">
<em class="property">class </em><code class="descclassname">sklearn.cross_validation.</code><code class="descname">KFold</code><span class="sig-paren">(</span><em>n</em>, <em>n_folds=3</em>, <em>shuffle=False</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cross_validation.py#L254"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_validation.KFold" title="Permalink to this definition">¶</a></dt>
<dd><p>K-Folds cross validation iterator.</p>
<p>Provides train/test indices to split data in train test sets. Split
dataset into k consecutive folds (without shuffling by default).</p>
<p>Each fold is then used a validation set once while the k - 1 remaining
fold form the training set.</p>
<p>Read more in the <a class="reference internal" href="../cross_validation.html#cross-validation"><span>User Guide</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>n</strong> : int</p>
<blockquote>
<div><p>Total number of elements.</p>
</div></blockquote>
<p><strong>n_folds</strong> : int, default=3</p>
<blockquote>
<div><p>Number of folds. Must be at least 2.</p>
</div></blockquote>
<p><strong>shuffle</strong> : boolean, optional</p>
<blockquote>
<div><p>Whether to shuffle the data before splitting into batches.</p>
</div></blockquote>
<p><strong>random_state</strong> : None, int or RandomState</p>
<blockquote class="last">
<div><p>When shuffle=True, pseudo-random number generator state used for
shuffling. If None, use default numpy RNG for shuffling.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="docutils">
<dt><a class="reference internal" href="sklearn.cross_validation.StratifiedKFold.html#sklearn.cross_validation.StratifiedKFold" title="sklearn.cross_validation.StratifiedKFold"><code class="xref py py-obj docutils literal"><span class="pre">StratifiedKFold</span></code></a></dt>
<dd>take label information into account to avoid building</dd>
</dl>
<p><code class="xref py py-obj docutils literal"><span class="pre">folds</span></code>, <code class="xref py py-obj docutils literal"><span class="pre">classification</span></code></p>
<dl class="last docutils">
<dt><a class="reference internal" href="sklearn.cross_validation.LabelKFold.html#sklearn.cross_validation.LabelKFold" title="sklearn.cross_validation.LabelKFold"><code class="xref py py-obj docutils literal"><span class="pre">LabelKFold</span></code></a></dt>
<dd>K-fold iterator variant with non-overlapping labels.</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The first n % n_folds folds have size n // n_folds + 1, other folds have
size n // n_folds.</p>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">KFold</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">n_folds</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">kf</span><span class="p">)</span>
<span class="go">2</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">kf</span><span class="p">)</span>
<span class="go">sklearn.cross_validation.KFold(n=4, n_folds=2, shuffle=False,</span>
<span class="go"> random_state=None)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">train_index</span><span class="p">,</span> <span class="n">test_index</span> <span class="ow">in</span> <span class="n">kf</span><span class="p">:</span>
<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="s2">"TRAIN:"</span><span class="p">,</span> <span class="n">train_index</span><span class="p">,</span> <span class="s2">"TEST:"</span><span class="p">,</span> <span class="n">test_index</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="gp">... </span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="go">TRAIN: [2 3] TEST: [0 1]</span>
<span class="go">TRAIN: [0 1] TEST: [2 3]</span>
<span class="go">.. automethod:: __init__</span>
</pre></div>
</div>
</dd></dl>
<div class="section" id="examples-using-sklearn-cross-validation-kfold">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cross_validation.KFold</span></code><a class="headerlink" href="#examples-using-sklearn-cross-validation-kfold" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="This example compares 2 dimensionality reduction strategies:"><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html"><img alt="../../_images/plot_feature_agglomeration_vs_univariate_selection1.png" src="../../_images/plot_feature_agglomeration_vs_univariate_selection1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#example-cluster-plot-feature-agglomeration-vs-univariate-selection-py"><span>Feature agglomeration vs. univariate selection</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Out-of-bag (OOB) estimates can be a useful heuristic to estimate the "optimal" number of boosti..."><div class="figure" id="id2">
<a class="reference external image-reference" href="./../../auto_examples/ensemble/plot_gradient_boosting_oob.html"><img alt="../../_images/plot_gradient_boosting_oob1.png" src="../../_images/plot_gradient_boosting_oob1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_oob.html#example-ensemble-plot-gradient-boosting-oob-py"><span>Gradient Boosting Out-of-Bag estimates</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="A tutorial exercise which uses cross-validation with linear models."><div class="figure" id="id3">
<a class="reference external image-reference" href="./../../auto_examples/exercises/plot_cv_diabetes.html"><img alt="../../_images/plot_cv_diabetes1.png" src="../../_images/plot_cv_diabetes1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/exercises/plot_cv_diabetes.html#example-exercises-plot-cv-diabetes-py"><span>Cross-validation on diabetes Dataset Exercise</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="In this example, we fit a Gaussian Process model onto the diabetes dataset."><div class="figure" id="id4">
<a class="reference external image-reference" href="./../../auto_examples/gaussian_process/gp_diabetes_dataset.html"><img alt="../../_images/gp_diabetes_dataset1.png" src="../../_images/gp_diabetes_dataset1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/gaussian_process/gp_diabetes_dataset.html#example-gaussian-process-gp-diabetes-dataset-py"><span>Gaussian Processes regression: goodness-of-fit on the ‘diabetes’ dataset</span></a></span></p>
</div>
</div><div class="clearer"></div></div>
</div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
<div class="footer">
© 2010 - 2014, scikit-learn developers (BSD License).
<a href="../../_sources/modules/generated/sklearn.cross_validation.KFold.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="sklearn.covariance.graph_lasso.html">Previous
</a>
</div>
</div>
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-22606712-2']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
<script src="http://www.google.com/jsapi" type="text/javascript"></script>
<script type="text/javascript"> google.load('search', '1',
{language : 'en'}); google.setOnLoadCallback(function() {
var customSearchControl = new
google.search.CustomSearchControl('016639176250731907682:tjtqbvtvij0');
customSearchControl.setResultSetSize(google.search.Search.FILTERED_CSE_RESULTSET);
var options = new google.search.DrawOptions();
options.setAutoComplete(true);
customSearchControl.draw('cse', options); }, true);
</script>
<script src="https://scikit-learn.org/versionwarning.js"></script>
</body>
</html>