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common_pitfalls.html
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<li class="toctree-l1 has-children"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html">1.1. Linear Models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<li class="toctree-l2"><a class="reference internal" href="computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
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</details></li>
<li class="toctree-l1"><a class="reference internal" href="model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/array_api.html">11.1. Array API support (experimental)</a></li>
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</details></li>
<li class="toctree-l1"><a class="reference internal" href="machine_learning_map.html">12. Choosing the right estimator</a></li>
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<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">>>> </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">>>> </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">>>> </span><span class="n">random_state</span> <span class="o">=</span> <span class="mi">42</span>
<span class="gp">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </span><span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="gp">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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=[('standardscaler', StandardScaler()),</span>
<span class="go"> ('linearregression', LinearRegression())])</span>
<span class="gp">>>> </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>
<section id="how-to-avoid-data-leakage">
<h3><span class="section-number">10.2.1. </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>
<p>An example of data leakage during preprocessing is detailed below.</p>
</section>
<section id="data-leakage-during-pre-processing">
<h3><span class="section-number">10.2.2. </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">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </span><span class="c1"># Incorrect preprocessing: the entire data is transformed</span>
<span class="gp">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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=[('selectkbest', SelectKBest(k=25)),</span>
<span class="go"> ('gradientboostingclassifier',</span>
<span class="go"> GradientBoostingClassifier(random_state=1))])</span>
<span class="gp">>>> </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">>>> </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">>>> </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">>>> </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">>>> </span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"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">"</span><span class="p">)</span>
<span class="go">Mean accuracy: 0.46+/-0.07</span>
</pre></div>
</div>
</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">>>> </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">>>> </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">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </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">>>> </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">>>> </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">>>> </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">>>> </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