-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathsklearn.cross_validation.cross_val_predict.html
311 lines (270 loc) · 14.1 KB
/
sklearn.cross_validation.cross_val_predict.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.cross_val_predict — 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.permutation_test_score" href="sklearn.cross_validation.permutation_test_score.html" />
<link rel="prev" title="sklearn.cross_validation.cross_val_score" href="sklearn.cross_validation.cross_val_score.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.cross_val_predict.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.cross_validation.cross_val_score.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
sklearn.cross...
</span>
<span class="hiddenrellink">
sklearn.cross_validation.cross_val_score
</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>.cross_val_predict</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cross-validation-cross-val-predict">Examples using <code class="docutils literal"><span class="pre">sklearn.cross_validation.cross_val_predict</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-cross-val-predict">
<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>.cross_val_predict<a class="headerlink" href="#sklearn-cross-validation-cross-val-predict" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.cross_validation.cross_val_predict">
<code class="descclassname">sklearn.cross_validation.</code><code class="descname">cross_val_predict</code><span class="sig-paren">(</span><em>estimator</em>, <em>X</em>, <em>y=None</em>, <em>cv=None</em>, <em>n_jobs=1</em>, <em>verbose=0</em>, <em>fit_params=None</em>, <em>pre_dispatch='2*n_jobs'</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cross_validation.py#L1181"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_validation.cross_val_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate cross-validated estimates for each input data point</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>estimator</strong> : estimator object implementing ‘fit’ and ‘predict’</p>
<blockquote>
<div><p>The object to use to fit the data.</p>
</div></blockquote>
<p><strong>X</strong> : array-like</p>
<blockquote>
<div><p>The data to fit. Can be, for example a list, or an array at least 2d.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, optional, default: None</p>
<blockquote>
<div><p>The target variable to try to predict in the case of
supervised learning.</p>
</div></blockquote>
<p><strong>cv</strong> : int, cross-validation generator or an iterable, optional</p>
<blockquote>
<div><p>Determines the cross-validation splitting strategy.
Possible inputs for cv are:</p>
<ul class="simple">
<li>None, to use the default 3-fold cross-validation,</li>
<li>integer, to specify the number of folds.</li>
<li>An object to be used as a cross-validation generator.</li>
<li>An iterable yielding train/test splits.</li>
</ul>
<p>For integer/None inputs, if <code class="docutils literal"><span class="pre">y</span></code> is binary or multiclass,
<a class="reference internal" href="sklearn.cross_validation.StratifiedKFold.html#sklearn.cross_validation.StratifiedKFold" title="sklearn.cross_validation.StratifiedKFold"><code class="xref py py-class docutils literal"><span class="pre">StratifiedKFold</span></code></a> used. If the estimator is a classifier
or if <code class="docutils literal"><span class="pre">y</span></code> is neither binary nor multiclass, <a class="reference internal" href="sklearn.cross_validation.KFold.html#sklearn.cross_validation.KFold" title="sklearn.cross_validation.KFold"><code class="xref py py-class docutils literal"><span class="pre">KFold</span></code></a> is used.</p>
<p>Refer <a class="reference internal" href="../cross_validation.html#cross-validation"><span>User Guide</span></a> for the various
cross-validation strategies that can be used here.</p>
</div></blockquote>
<p><strong>n_jobs</strong> : integer, optional</p>
<blockquote>
<div><p>The number of CPUs to use to do the computation. -1 means
‘all CPUs’.</p>
</div></blockquote>
<p><strong>verbose</strong> : integer, optional</p>
<blockquote>
<div><p>The verbosity level.</p>
</div></blockquote>
<p><strong>fit_params</strong> : dict, optional</p>
<blockquote>
<div><p>Parameters to pass to the fit method of the estimator.</p>
</div></blockquote>
<p><strong>pre_dispatch</strong> : int, or string, optional</p>
<blockquote>
<div><p>Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:</p>
<blockquote>
<div><ul class="simple">
<li>None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs</li>
<li>An int, giving the exact number of total jobs that are
spawned</li>
<li>A string, giving an expression as a function of n_jobs,
as in ‘2*n_jobs’</li>
</ul>
</div></blockquote>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>preds</strong> : ndarray</p>
<blockquote class="last">
<div><p>This is the result of calling ‘predict’</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<div class="section" id="examples-using-sklearn-cross-validation-cross-val-predict">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cross_validation.cross_val_predict</span></code><a class="headerlink" href="#examples-using-sklearn-cross-validation-cross-val-predict" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="This example shows how to use `cross_val_predict` to visualize prediction errors."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/./plot_cv_predict.html"><img alt="../../_images/plot_cv_predict1.png" src="../../_images/plot_cv_predict1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_cv_predict.html#example-plot-cv-predict-py"><span>Plotting Cross-Validated Predictions</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.cross_val_predict.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="sklearn.cross_validation.cross_val_score.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>