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</style><section id="common-pitfalls-and-recommended-practices">
<span id="common-pitfalls"></span><h1><span class="section-number">10. </span>Common pitfalls and recommended practices<a class="headerlink" href="#common-pitfalls-and-recommended-practices" title="Link to this heading">¶</a></h1>
<p>The purpose of this chapter is to illustrate some common pitfalls and
anti-patterns that occur when using scikit-learn. It provides
examples of what <strong>not</strong> to do, along with a corresponding correct
example.</p>
<section id="inconsistent-preprocessing">
<h2><span class="section-number">10.1. </span>Inconsistent preprocessing<a class="headerlink" href="#inconsistent-preprocessing" title="Link to this heading">¶</a></h2>
<p>scikit-learn provides a library of <a class="reference internal" href="data_transforms.html#data-transforms"><span class="std std-ref">Dataset transformations</span></a>, which
may clean (see <a class="reference internal" href="modules/preprocessing.html#preprocessing"><span class="std std-ref">Preprocessing data</span></a>), reduce
(see <a class="reference internal" href="modules/unsupervised_reduction.html#data-reduction"><span class="std std-ref">Unsupervised dimensionality reduction</span></a>), expand (see <a class="reference internal" href="modules/kernel_approximation.html#kernel-approximation"><span class="std std-ref">Kernel Approximation</span></a>)
or generate (see <a class="reference internal" href="modules/feature_extraction.html#feature-extraction"><span class="std std-ref">Feature extraction</span></a>) feature representations.
If these data transforms are used when training a model, they also
must be used on subsequent datasets, whether it’s test data or
data in a production system. Otherwise, the feature space will change,
and the model will not be able to perform effectively.</p>
<p>For the following example, let’s create a synthetic dataset with a
single feature:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">random_state</span> <span class="o">=</span> <span class="mi">42</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="gp">... </span>    <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>Wrong</strong></p>
<p>The train dataset is scaled, but not the test dataset, so model
performance on the test dataset is worse than expected:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_train_transformed</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_transformed</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
<span class="go">62.80...</span>
</pre></div>
</div>
<p><strong>Right</strong></p>
<p>Instead of passing the non-transformed <code class="docutils literal notranslate"><span class="pre">X_test</span></code> to <code class="docutils literal notranslate"><span class="pre">predict</span></code>, we should
transform the test data, the same way we transformed the training data:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X_test_transformed</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_transformed</span><span class="p">))</span>
<span class="go">0.90...</span>
</pre></div>
</div>
<p>Alternatively, we recommend using a <a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>, which makes it easier to chain transformations
with estimators, and reduces the possibility of forgetting a transformation:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">StandardScaler</span><span class="p">(),</span> <span class="n">LinearRegression</span><span class="p">())</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">Pipeline(steps=[(&#39;standardscaler&#39;, StandardScaler()),</span>
<span class="go">                (&#39;linearregression&#39;, LinearRegression())])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
<span class="go">0.90...</span>
</pre></div>
</div>
<p>Pipelines also help avoiding another common pitfall: leaking the test data
into the training data.</p>
</section>
<section id="data-leakage">
<span id="id1"></span><h2><span class="section-number">10.2. </span>Data leakage<a class="headerlink" href="#data-leakage" title="Link to this heading">¶</a></h2>
<p>Data leakage occurs when information that would not be available at prediction
time is used when building the model. This results in overly optimistic
performance estimates, for example from <a class="reference internal" href="modules/cross_validation.html#cross-validation"><span class="std std-ref">cross-validation</span></a>, and thus poorer performance when the model is used
on actually novel data, for example during production.</p>
<p>A common cause is not keeping the test and train data subsets separate.
Test data should never be used to make choices about the model.
<strong>The general rule is to never call</strong> <code class="docutils literal notranslate"><span class="pre">fit</span></code> <strong>on the test data</strong>. While this
may sound obvious, this is easy to miss in some cases, for example when
applying certain pre-processing steps.</p>
<p>Although both train and test data subsets should receive the same
preprocessing transformation (as described in the previous section), it is
important that these transformations are only learnt from the training data.
For example, if you have a
normalization step where you divide by the average value, the average should
be the average of the train subset, <strong>not</strong> the average of all the data. If the
test subset is included in the average calculation, information from the test
subset is influencing the model.</p>
<p>An example of data leakage during preprocessing is detailed below.</p>
<section id="data-leakage-during-pre-processing">
<h3><span class="section-number">10.2.1. </span>Data leakage during pre-processing<a class="headerlink" href="#data-leakage-during-pre-processing" title="Link to this heading">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We here choose to illustrate data leakage with a feature selection step.
This risk of leakage is however relevant with almost all transformations
in scikit-learn, including (but not limited to)
<a class="reference internal" href="modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">StandardScaler</span></code></a>,
<a class="reference internal" href="modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">SimpleImputer</span></code></a>, and
<a class="reference internal" href="modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">PCA</span></code></a>.</p>
</div>
<p>A number of <a class="reference internal" href="modules/feature_selection.html#feature-selection"><span class="std std-ref">Feature selection</span></a> functions are available in scikit-learn.
They can help remove irrelevant, redundant and noisy features as well as
improve your model build time and performance. As with any other type of
preprocessing, feature selection should <strong>only</strong> use the training data.
Including the test data in feature selection will optimistically bias your
model.</p>
<p>To demonstrate we will create this binary classification problem with
10,000 randomly generated features:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">,</span> <span class="n">n_classes</span> <span class="o">=</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">10000</span><span class="p">,</span> <span class="mi">2</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>Wrong</strong></p>
<p>Using all the data to perform feature selection results in an accuracy score
much higher than chance, even though our targets are completely random.
This randomness means that our <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> are independent and we thus expect
the accuracy to be around 0.5. However, since the feature selection step
‘sees’ the test data, the model has an unfair advantage. In the incorrect
example below we first use all the data for feature selection and then split
the data into training and test subsets for model fitting. The result is a
much higher than expected accuracy score:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <span class="n">SelectKBest</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>

<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Incorrect preprocessing: the entire data is transformed</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_selected</span> <span class="o">=</span> <span class="n">SelectKBest</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="gp">... </span>    <span class="n">X_selected</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">gbc</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">gbc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">GradientBoostingClassifier(random_state=1)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">gbc</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="go">0.76</span>
</pre></div>
</div>
<p><strong>Right</strong></p>
<p>To prevent data leakage, it is good practice to split your data into train
and test subsets <strong>first</strong>. Feature selection can then be formed using just
the train dataset. Notice that whenever we use <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>, we
only use the train dataset. The score is now what we would expect for the
data, close to chance:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="gp">... </span>    <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">select</span> <span class="o">=</span> <span class="n">SelectKBest</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_train_selected</span> <span class="o">=</span> <span class="n">select</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">gbc</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">gbc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_selected</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">GradientBoostingClassifier(random_state=1)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">X_test_selected</span> <span class="o">=</span> <span class="n">select</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">gbc</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_selected</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="go">0.46</span>
</pre></div>
</div>
<p>Here again, we recommend using a <a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> to chain
together the feature selection and model estimators. The pipeline ensures
that only the training data is used when performing <code class="docutils literal notranslate"><span class="pre">fit</span></code> and the test data
is used only for calculating the accuracy score:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="gp">... </span>    <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">pipeline</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">SelectKBest</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">25</span><span class="p">),</span>
<span class="gp">... </span>                         <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">Pipeline(steps=[(&#39;selectkbest&#39;, SelectKBest(k=25)),</span>
<span class="go">                (&#39;gradientboostingclassifier&#39;,</span>
<span class="go">                GradientBoostingClassifier(random_state=1))])</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="go">0.46</span>
</pre></div>
</div>
<p>The pipeline can also be fed into a cross-validation
function such as <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_val_score</span></code></a>.
Again, the pipeline ensures that the correct data subset and estimator
method is used during fitting and predicting:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">pipeline</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Mean accuracy: </span><span class="si">{</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">+/-</span><span class="si">{</span><span class="n">scores</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="go">Mean accuracy: 0.46+/-0.07</span>
</pre></div>
</div>
</section>
<section id="how-to-avoid-data-leakage">
<h3><span class="section-number">10.2.2. </span>How to avoid data leakage<a class="headerlink" href="#how-to-avoid-data-leakage" title="Link to this heading">¶</a></h3>
<p>Below are some tips on avoiding data leakage:</p>
<ul>
<li><p>Always split the data into train and test subsets first, particularly
before any preprocessing steps.</p></li>
<li><p>Never include test data when using the <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>
methods. Using all the data, e.g., <code class="docutils literal notranslate"><span class="pre">fit(X)</span></code>, can result in overly optimistic
scores.</p>
<p>Conversely, the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method should be used on both train and test
subsets as the same preprocessing should be applied to all the data.
This can be achieved by using <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code> on the train subset and
<code class="docutils literal notranslate"><span class="pre">transform</span></code> on the test subset.</p>
</li>
<li><p>The scikit-learn <a class="reference internal" href="modules/compose.html#pipeline"><span class="std std-ref">pipeline</span></a> is a great way to prevent data
leakage as it ensures that the appropriate method is performed on the
correct data subset. The pipeline is ideal for use in cross-validation
and hyper-parameter tuning functions.</p></li>
</ul>
</section>
</section>
<section id="controlling-randomness">
<span id="randomness"></span><h2><span class="section-number">10.3. </span>Controlling randomness<a class="headerlink" href="#controlling-randomness" title="Link to this heading">¶</a></h2>
<p>Some scikit-learn objects are inherently random. These are usually estimators
(e.g. <a class="reference internal" href="modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a>) and cross-validation
splitters (e.g. <a class="reference internal" href="modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code></a>). The randomness of
these objects is controlled via their <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter, as described
in the <a class="reference internal" href="glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>. This section expands on the glossary
entry, and describes good practices and common pitfalls w.r.t. this
subtle parameter.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Recommendation summary</p>
<p>For an optimal robustness of cross-validation (CV) results, pass
<code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances when creating estimators, or leave <code class="docutils literal notranslate"><span class="pre">random_state</span></code>
to <code class="docutils literal notranslate"><span class="pre">None</span></code>. Passing integers to CV splitters is usually the safest option
and is preferable; passing <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances to splitters may
sometimes be useful to achieve very specific use-cases.
For both estimators and splitters, passing an integer vs passing an
instance (or <code class="docutils literal notranslate"><span class="pre">None</span></code>) leads to subtle but significant differences,
especially for CV procedures. These differences are important to
understand when reporting results.</p>
<p>For reproducible results across executions, remove any use of
<code class="docutils literal notranslate"><span class="pre">random_state=None</span></code>.</p>
</div>
<section id="using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split">
<h3><span class="section-number">10.3.1. </span>Using <code class="docutils literal notranslate"><span class="pre">None</span></code> or <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances, and repeated calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">split</span></code><a class="headerlink" href="#using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split" title="Link to this heading">¶</a></h3>
<p>The <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter determines whether multiple calls to <a class="reference internal" href="glossary.html#term-fit"><span class="xref std std-term">fit</span></a>
(for estimators) or to <a class="reference internal" href="glossary.html#term-split"><span class="xref std std-term">split</span></a> (for CV splitters) will produce the same
results, according to these rules:</p>
<ul class="simple">
<li><p>If an integer is passed, calling <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">split</span></code> multiple times always
yields the same results.</p></li>
<li><p>If <code class="docutils literal notranslate"><span class="pre">None</span></code> or a <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instance is passed: <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">split</span></code> will
yield different results each time they are called, and the succession of
calls explores all sources of entropy. <code class="docutils literal notranslate"><span class="pre">None</span></code> is the default value for all
<code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameters.</p></li>
</ul>
<p>We here illustrate these rules for both estimators and CV splitters.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Since passing <code class="docutils literal notranslate"><span class="pre">random_state=None</span></code> is equivalent to passing the global
<code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instance from <code class="docutils literal notranslate"><span class="pre">numpy</span></code>
(<code class="docutils literal notranslate"><span class="pre">random_state=np.random.mtrand._rand</span></code>), we will not explicitly mention
<code class="docutils literal notranslate"><span class="pre">None</span></code> here. Everything that applies to instances also applies to using
<code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
</div>
<section id="estimators">
<h4><span class="section-number">10.3.1.1. </span>Estimators<a class="headerlink" href="#estimators" title="Link to this heading">¶</a></h4>
<p>Passing instances means that calling <code class="docutils literal notranslate"><span class="pre">fit</span></code> multiple times will not yield the
same results, even if the estimator is fitted on the same data and with the
same hyper-parameters:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">sgd</span> <span class="o">=</span> <span class="n">SGDClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">sgd</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">coef_</span>
<span class="go">array([[ 8.85418642,  4.79084103, -3.13077794,  8.11915045, -0.56479934]])</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">sgd</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">coef_</span>
<span class="go">array([[ 6.70814003,  5.25291366, -7.55212743,  5.18197458,  1.37845099]])</span>
</pre></div>
</div>
<p>We can see from the snippet above that repeatedly calling <code class="docutils literal notranslate"><span class="pre">sgd.fit</span></code> has
produced different models, even if the data was the same. This is because the
Random Number Generator (RNG) of the estimator is consumed (i.e. mutated)
when <code class="docutils literal notranslate"><span class="pre">fit</span></code> is called, and this mutated RNG will be used in the subsequent
calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>. In addition, the <code class="docutils literal notranslate"><span class="pre">rng</span></code> object is shared across all objects
that use it, and as a consequence, these objects become somewhat
inter-dependent. For example, two estimators that share the same
<code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instance will influence each other, as we will see later when
we discuss cloning. This point is important to keep in mind when debugging.</p>
<p>If we had passed an integer to the <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter of the
<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 notranslate"><span class="pre">SGDClassifier</span></code></a>, we would have obtained the
same models, and thus the same scores each time. When we pass an integer, the
same RNG is used across all calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>. What internally happens is that
even though the RNG is consumed when <code class="docutils literal notranslate"><span class="pre">fit</span></code> is called, it is always reset to
its original state at the beginning of <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</section>
<section id="cv-splitters">
<h4><span class="section-number">10.3.1.2. </span>CV splitters<a class="headerlink" href="#cv-splitters" title="Link to this heading">¶</a></h4>
<p>Randomized CV splitters have a similar behavior when a <code class="docutils literal notranslate"><span class="pre">RandomState</span></code>
instance is passed; calling <code class="docutils literal notranslate"><span class="pre">split</span></code> multiple times yields different data
splits:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">KFold</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cv</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
<span class="go">[0 3 5 6 7] [1 2 4 8 9]</span>
<span class="go">[1 2 4 8 9] [0 3 5 6 7]</span>

<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
<span class="go">[0 4 6 7 8] [1 2 3 5 9]</span>
<span class="go">[1 2 3 5 9] [0 4 6 7 8]</span>
</pre></div>
</div>
<p>We can see that the splits are different from the second time <code class="docutils literal notranslate"><span class="pre">split</span></code> is
called. This may lead to unexpected results if you compare the performance of
multiple estimators by calling <code class="docutils literal notranslate"><span class="pre">split</span></code> many times, as we will see in the next
section.</p>
</section>
</section>
<section id="common-pitfalls-and-subtleties">
<h3><span class="section-number">10.3.2. </span>Common pitfalls and subtleties<a class="headerlink" href="#common-pitfalls-and-subtleties" title="Link to this heading">¶</a></h3>
<p>While the rules that govern the <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter are seemingly simple,
they do however have some subtle implications. In some cases, this can even
lead to wrong conclusions.</p>
<section id="id2">
<h4><span class="section-number">10.3.2.1. </span>Estimators<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h4>
<p><strong>Different `random_state` types lead to different cross-validation
procedures</strong></p>
<p>Depending on the type of the <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter, estimators will behave
differently, especially in cross-validation procedures. Consider the
following snippet:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">rf_123</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">rf_123</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">array([0.85, 0.95, 0.95, 0.9 , 0.9 ])</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">rf_inst</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">rf_inst</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">array([0.9 , 0.95, 0.95, 0.9 , 0.9 ])</span>
</pre></div>
</div>
<p>We see that the cross-validated scores of <code class="docutils literal notranslate"><span class="pre">rf_123</span></code> and <code class="docutils literal notranslate"><span class="pre">rf_inst</span></code> are
different, as should be expected since we didn’t pass the same <code class="docutils literal notranslate"><span class="pre">random_state</span></code>
parameter. However, the difference between these scores is more subtle than
it looks, and <strong>the cross-validation procedures that were performed by</strong>
<a class="reference internal" href="modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_val_score</span></code></a> <strong>significantly differ in
each case</strong>:</p>
<ul class="simple">
<li><p>Since <code class="docutils literal notranslate"><span class="pre">rf_123</span></code> was passed an integer, every call to <code class="docutils literal notranslate"><span class="pre">fit</span></code> uses the same RNG:
this means that all random characteristics of the random forest estimator
will be the same for each of the 5 folds of the CV procedure. In
particular, the (randomly chosen) subset of features of the estimator will
be the same across all folds.</p></li>
<li><p>Since <code class="docutils literal notranslate"><span class="pre">rf_inst</span></code> was passed a <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instance, each call to <code class="docutils literal notranslate"><span class="pre">fit</span></code>
starts from a different RNG. As a result, the random subset of features
will be different for each folds.</p></li>
</ul>
<p>While having a constant estimator RNG across folds isn’t inherently wrong, we
usually want CV results that are robust w.r.t. the estimator’s randomness. As
a result, passing an instance instead of an integer may be preferable, since
it will allow the estimator RNG to vary for each fold.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Here, <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_val_score</span></code></a> will use a
non-randomized CV splitter (as is the default), so both estimators will
be evaluated on the same splits. This section is not about variability in
the splits. Also, whether we pass an integer or an instance to
<a class="reference internal" href="modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_classification</span></code></a> isn’t relevant for our
illustration purpose: what matters is what we pass to the
<a class="reference internal" href="modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a> estimator.</p>
</div>
<p><strong>Cloning</strong></p>
<p>Another subtle side effect of passing <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances is how
<a class="reference internal" href="modules/generated/sklearn.base.clone.html#sklearn.base.clone" title="sklearn.base.clone"><code class="xref py py-func docutils literal notranslate"><span class="pre">clone</span></code></a> will work:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">clone</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="n">clone</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
</pre></div>
</div>
<p>Since a <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instance was passed to <code class="docutils literal notranslate"><span class="pre">a</span></code>, <code class="docutils literal notranslate"><span class="pre">a</span></code> and <code class="docutils literal notranslate"><span class="pre">b</span></code> are not clones
in the strict sense, but rather clones in the statistical sense: <code class="docutils literal notranslate"><span class="pre">a</span></code> and <code class="docutils literal notranslate"><span class="pre">b</span></code>
will still be different models, even when calling <code class="docutils literal notranslate"><span class="pre">fit(X,</span> <span class="pre">y)</span></code> on the same
data. Moreover, <code class="docutils literal notranslate"><span class="pre">a</span></code> and <code class="docutils literal notranslate"><span class="pre">b</span></code> will influence each-other since they share the
same internal RNG: calling <code class="docutils literal notranslate"><span class="pre">a.fit</span></code> will consume <code class="docutils literal notranslate"><span class="pre">b</span></code>’s RNG, and calling
<code class="docutils literal notranslate"><span class="pre">b.fit</span></code> will consume <code class="docutils literal notranslate"><span class="pre">a</span></code>’s RNG, since they are the same. This bit is true for
any estimators that share a <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter; it is not specific to
clones.</p>
<p>If an integer were passed, <code class="docutils literal notranslate"><span class="pre">a</span></code> and <code class="docutils literal notranslate"><span class="pre">b</span></code> would be exact clones and they would not
influence each other.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Even though <a class="reference internal" href="modules/generated/sklearn.base.clone.html#sklearn.base.clone" title="sklearn.base.clone"><code class="xref py py-func docutils literal notranslate"><span class="pre">clone</span></code></a> is rarely used in user code, it is
called pervasively throughout scikit-learn codebase: in particular, most
meta-estimators that accept non-fitted estimators call
<a class="reference internal" href="modules/generated/sklearn.base.clone.html#sklearn.base.clone" title="sklearn.base.clone"><code class="xref py py-func docutils literal notranslate"><span class="pre">clone</span></code></a> internally
(<a class="reference internal" href="modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>,
<a class="reference internal" href="modules/generated/sklearn.ensemble.StackingClassifier.html#sklearn.ensemble.StackingClassifier" title="sklearn.ensemble.StackingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">StackingClassifier</span></code></a>,
<a class="reference internal" href="modules/generated/sklearn.calibration.CalibratedClassifierCV.html#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">CalibratedClassifierCV</span></code></a>, etc.).</p>
</div>
</section>
<section id="id3">
<h4><span class="section-number">10.3.2.2. </span>CV splitters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h4>
<p>When passed a <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instance, CV splitters yield different splits
each time <code class="docutils literal notranslate"><span class="pre">split</span></code> is called. When comparing different estimators, this can
lead to overestimating the variance of the difference in performance between
the estimators:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">KFold</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cv</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">lda</span> <span class="o">=</span> <span class="n">LinearDiscriminantAnalysis</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nb</span> <span class="o">=</span> <span class="n">GaussianNB</span><span class="p">()</span>

<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">est</span> <span class="ow">in</span> <span class="p">(</span><span class="n">lda</span><span class="p">,</span> <span class="n">nb</span><span class="p">):</span>
<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">est</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">))</span>
<span class="go">[0.8  0.75 0.75 0.7  0.85]</span>
<span class="go">[0.85 0.95 0.95 0.85 0.95]</span>
</pre></div>
</div>
<p>Directly comparing the performance of the
<a class="reference internal" href="modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearDiscriminantAnalysis</span></code></a> estimator
vs the <a class="reference internal" href="modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianNB</span></code></a> estimator <strong>on each fold</strong> would
be a mistake: <strong>the splits on which the estimators are evaluated are
different</strong>. Indeed, <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_val_score</span></code></a> will
internally call <code class="docutils literal notranslate"><span class="pre">cv.split</span></code> on the same
<a class="reference internal" href="modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code></a> instance, but the splits will be
different each time. This is also true for any tool that performs model
selection via cross-validation, e.g.
<a class="reference internal" href="modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a> and
<a class="reference internal" href="modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomizedSearchCV</span></code></a>: scores are not
comparable fold-to-fold across different calls to <code class="docutils literal notranslate"><span class="pre">search.fit</span></code>, since
<code class="docutils literal notranslate"><span class="pre">cv.split</span></code> would have been called multiple times. Within a single call to
<code class="docutils literal notranslate"><span class="pre">search.fit</span></code>, however, fold-to-fold comparison is possible since the search
estimator only calls <code class="docutils literal notranslate"><span class="pre">cv.split</span></code> once.</p>
<p>For comparable fold-to-fold results in all scenarios, one should pass an
integer to the CV splitter: <code class="docutils literal notranslate"><span class="pre">cv</span> <span class="pre">=</span> <span class="pre">KFold(shuffle=True,</span> <span class="pre">random_state=0)</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>While fold-to-fold comparison is not advisable with <code class="docutils literal notranslate"><span class="pre">RandomState</span></code>
instances, one can however expect that average scores allow to conclude
whether one estimator is better than another, as long as enough folds and
data are used.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>What matters in this example is what was passed to
<a class="reference internal" href="modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code></a>. Whether we pass a <code class="docutils literal notranslate"><span class="pre">RandomState</span></code>
instance or an integer to <a class="reference internal" href="modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_classification</span></code></a>
is not relevant for our illustration purpose. Also, neither
<a class="reference internal" href="modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearDiscriminantAnalysis</span></code></a> nor
<a class="reference internal" href="modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianNB</span></code></a> are randomized estimators.</p>
</div>
</section>
</section>
<section id="general-recommendations">
<h3><span class="section-number">10.3.3. </span>General recommendations<a class="headerlink" href="#general-recommendations" title="Link to this heading">¶</a></h3>
<section id="getting-reproducible-results-across-multiple-executions">
<h4><span class="section-number">10.3.3.1. </span>Getting reproducible results across multiple executions<a class="headerlink" href="#getting-reproducible-results-across-multiple-executions" title="Link to this heading">¶</a></h4>
<p>In order to obtain reproducible (i.e. constant) results across multiple
<em>program executions</em>, we need to remove all uses of <code class="docutils literal notranslate"><span class="pre">random_state=None</span></code>, which
is the default. The recommended way is to declare a <code class="docutils literal notranslate"><span class="pre">rng</span></code> variable at the top
of the program, and pass it down to any object that accepts a <code class="docutils literal notranslate"><span class="pre">random_state</span></code>
parameter:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span>                                                    <span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">rf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="go">0.84</span>
</pre></div>
</div>
<p>We are now guaranteed that the result of this script will always be 0.84, no
matter how many times we run it. Changing the global <code class="docutils literal notranslate"><span class="pre">rng</span></code> variable to a
different value should affect the results, as expected.</p>
<p>It is also possible to declare the <code class="docutils literal notranslate"><span class="pre">rng</span></code> variable as an integer. This may
however lead to less robust cross-validation results, as we will see in the
next section.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We do not recommend setting the global <code class="docutils literal notranslate"><span class="pre">numpy</span></code> seed by calling
<code class="docutils literal notranslate"><span class="pre">np.random.seed(0)</span></code>. See <a class="reference external" href="https://stackoverflow.com/questions/5836335/consistently-create-same-random-numpy-array/5837352#comment6712034_5837352">here</a>
for a discussion.</p>
</div>
</section>
<section id="robustness-of-cross-validation-results">
<h4><span class="section-number">10.3.3.2. </span>Robustness of cross-validation results<a class="headerlink" href="#robustness-of-cross-validation-results" title="Link to this heading">¶</a></h4>
<p>When we evaluate a randomized estimator performance by cross-validation, we
want to make sure that the estimator can yield accurate predictions for new
data, but we also want to make sure that the estimator is robust w.r.t. its
random initialization. For example, we would like the random weights
initialization of a <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 notranslate"><span class="pre">SGDClassifier</span></code></a> to be
consistently good across all folds: otherwise, when we train that estimator
on new data, we might get unlucky and the random initialization may lead to
bad performance. Similarly, we want a random forest to be robust w.r.t the
set of randomly selected features that each tree will be using.</p>
<p>For these reasons, it is preferable to evaluate the cross-validation
performance by letting the estimator use a different RNG on each fold. This
is done by passing a <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instance (or <code class="docutils literal notranslate"><span class="pre">None</span></code>) to the estimator
initialization.</p>
<p>When we pass an integer, the estimator will use the same RNG on each fold:
if the estimator performs well (or bad), as evaluated by CV, it might just be
because we got lucky (or unlucky) with that specific seed. Passing instances
leads to more robust CV results, and makes the comparison between various
algorithms fairer. It also helps limiting the temptation to treat the
estimator’s RNG as a hyper-parameter that can be tuned.</p>
<p>Whether we pass <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances or integers to CV splitters has no
impact on robustness, as long as <code class="docutils literal notranslate"><span class="pre">split</span></code> is only called once. When <code class="docutils literal notranslate"><span class="pre">split</span></code>
is called multiple times, fold-to-fold comparison isn’t possible anymore. As
a result, passing integer to CV splitters is usually safer and covers most
use-cases.</p>
</section>
</section>
</section>
</section>


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