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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cross_validation</span></code>.cross_val_predict</a></li>
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<div class="section" id="sklearn-cross-validation-cross-val-predict">
<h1><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cross_validation</span></code>.cross_val_predict<a class="headerlink" href="#sklearn-cross-validation-cross-val-predict" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last"><strong>DEPRECATED</strong></p>
</div>
<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/474b5e0/sklearn/cross_validation.py#L1294"><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>
<div class="deprecated">
<p><span class="versionmodified">Deprecated since version 0.18: </span>This module will be removed in 0.20.
Use <a class="reference internal" href="sklearn.model_selection.cross_val_predict.html#sklearn.model_selection.cross_val_predict" title="sklearn.model_selection.cross_val_predict"><code class="xref py py-func docutils literal"><span class="pre">sklearn.model_selection.cross_val_predict</span></code></a> instead.</p>
</div>
<p>Read more in the <a class="reference internal" href="../cross_validation.html#cross-validation"><span class="std std-ref">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 the estimator is a classifier and <code class="docutils literal"><span class="pre">y</span></code> is
either 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> is used. In all
other cases, <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 class="std std-ref">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>
<p class="rubric">Examples</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">linear_model</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="k">import</span> <span class="n">cross_val_predict</span>
<span class="gp">>>> </span><span class="n">diabetes</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_diabetes</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">diabetes</span><span class="o">.</span><span class="n">data</span><span class="p">[:</span><span class="mi">150</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">diabetes</span><span class="o">.</span><span class="n">target</span><span class="p">[:</span><span class="mi">150</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">lasso</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">Lasso</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">cross_val_predict</span><span class="p">(</span><span class="n">lasso</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
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