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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>
<li class="toctree-l2"><a class="reference internal" href="../modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/isotonic.html">1.15. Isotonic regression</a></li>
<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/mixture.html">2.1. Gaussian mixture models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<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/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<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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</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets.html">7. Dataset loading utilities</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="../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-l1 current active has-children"><a class="reference internal" href="../computing.html">8. Computing with scikit-learn</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2 current active"><a class="current reference internal" href="#">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">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>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">13. External Resources, Videos and Talks</a></li>
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<section id="strategies-to-scale-computationally-bigger-data">
<span id="scaling-strategies"></span><h1><span class="section-number">8.1. </span>Strategies to scale computationally: bigger data<a class="headerlink" href="#strategies-to-scale-computationally-bigger-data" title="Link to this heading">#</a></h1>
<p>For some applications the amount of examples, features (or both) and/or the
speed at which they need to be processed are challenging for traditional
approaches. In these cases scikit-learn has a number of options you can
consider to make your system scale.</p>
<section id="scaling-with-instances-using-out-of-core-learning">
<h2><span class="section-number">8.1.1. </span>Scaling with instances using out-of-core learning<a class="headerlink" href="#scaling-with-instances-using-out-of-core-learning" title="Link to this heading">#</a></h2>
<p>Out-of-core (or “external memory”) learning is a technique used to learn from
data that cannot fit in a computer’s main memory (RAM).</p>
<p>Here is a sketch of a system designed to achieve this goal:</p>
<ol class="arabic simple">
<li><p>a way to stream instances</p></li>
<li><p>a way to extract features from instances</p></li>
<li><p>an incremental algorithm</p></li>
</ol>
<section id="streaming-instances">
<h3><span class="section-number">8.1.1.1. </span>Streaming instances<a class="headerlink" href="#streaming-instances" title="Link to this heading">#</a></h3>
<p>Basically, 1. may be a reader that yields instances from files on a
hard drive, a database, from a network stream etc. However,
details on how to achieve this are beyond the scope of this documentation.</p>
</section>
<section id="extracting-features">
<h3><span class="section-number">8.1.1.2. </span>Extracting features<a class="headerlink" href="#extracting-features" title="Link to this heading">#</a></h3>
<p>2. could be any relevant way to extract features among the
different <a class="reference internal" href="../modules/feature_extraction.html#feature-extraction"><span class="std std-ref">feature extraction</span></a> methods supported by
scikit-learn. However, when working with data that needs vectorization and
where the set of features or values is not known in advance one should take
explicit care. A good example is text classification where unknown terms are
likely to be found during training. It is possible to use a stateful
vectorizer if making multiple passes over the data is reasonable from an
application point of view. Otherwise, one can turn up the difficulty by using
a stateless feature extractor. Currently the preferred way to do this is to
use the so-called <a class="reference internal" href="../modules/feature_extraction.html#feature-hashing"><span class="std std-ref">hashing trick</span></a> as implemented by
<a class="reference internal" href="../modules/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher" title="sklearn.feature_extraction.FeatureHasher"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_extraction.FeatureHasher</span></code></a> for datasets with categorical
variables represented as list of Python dicts or
<a class="reference internal" href="../modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html#sklearn.feature_extraction.text.HashingVectorizer" title="sklearn.feature_extraction.text.HashingVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_extraction.text.HashingVectorizer</span></code></a> for text documents.</p>
</section>
<section id="incremental-learning">
<h3><span class="section-number">8.1.1.3. </span>Incremental learning<a class="headerlink" href="#incremental-learning" title="Link to this heading">#</a></h3>
<p>Finally, for 3. we have a number of options inside scikit-learn. Although not
all algorithms can learn incrementally (i.e. without seeing all the instances
at once), all estimators implementing the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> API are candidates.
Actually, the ability to learn incrementally from a mini-batch of instances
(sometimes called “online learning”) is key to out-of-core learning as it
guarantees that at any given time there will be only a small amount of
instances in the main memory. Choosing a good size for the mini-batch that
balances relevancy and memory footprint could involve some tuning <a class="footnote-reference brackets" href="#id2" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
<p>Here is a list of incremental estimators for different tasks:</p>
<ul class="simple">
<li><dl class="simple">
<dt>Classification</dt><dd><ul>
<li><p><a class="reference internal" href="../modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB" title="sklearn.naive_bayes.MultinomialNB"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.naive_bayes.MultinomialNB</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB" title="sklearn.naive_bayes.BernoulliNB"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.naive_bayes.BernoulliNB</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.Perceptron.html#sklearn.linear_model.Perceptron" title="sklearn.linear_model.Perceptron"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.Perceptron</span></code></a></p></li>
<li><p><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">sklearn.linear_model.SGDClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model.PassiveAggressiveClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.PassiveAggressiveClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network.MLPClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.neural_network.MLPClassifier</span></code></a></p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>Regression</dt><dd><ul>
<li><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model.SGDRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.SGDRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model.PassiveAggressiveRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.PassiveAggressiveRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network.MLPRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.neural_network.MLPRegressor</span></code></a></p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>Clustering</dt><dd><ul>
<li><p><a class="reference internal" href="../modules/generated/sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster.MiniBatchKMeans"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.cluster.MiniBatchKMeans</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.cluster.Birch.html#sklearn.cluster.Birch" title="sklearn.cluster.Birch"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.cluster.Birch</span></code></a></p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>Decomposition / feature Extraction</dt><dd><ul>
<li><p><a class="reference internal" href="../modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html#sklearn.decomposition.MiniBatchDictionaryLearning" title="sklearn.decomposition.MiniBatchDictionaryLearning"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.decomposition.MiniBatchDictionaryLearning</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.decomposition.IncrementalPCA.html#sklearn.decomposition.IncrementalPCA" title="sklearn.decomposition.IncrementalPCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.decomposition.IncrementalPCA</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.decomposition.LatentDirichletAllocation.html#sklearn.decomposition.LatentDirichletAllocation" title="sklearn.decomposition.LatentDirichletAllocation"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.decomposition.LatentDirichletAllocation</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.decomposition.MiniBatchNMF.html#sklearn.decomposition.MiniBatchNMF" title="sklearn.decomposition.MiniBatchNMF"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.decomposition.MiniBatchNMF</span></code></a></p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>Preprocessing</dt><dd><ul>
<li><p><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">sklearn.preprocessing.StandardScaler</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler" title="sklearn.preprocessing.MinMaxScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.preprocessing.MinMaxScaler</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler" title="sklearn.preprocessing.MaxAbsScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.preprocessing.MaxAbsScaler</span></code></a></p></li>
</ul>
</dd>
</dl>
</li>
</ul>
<p>For classification, a somewhat important thing to note is that although a
stateless feature extraction routine may be able to cope with new/unseen
attributes, the incremental learner itself may be unable to cope with
new/unseen targets classes. In this case you have to pass all the possible
classes to the first <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> call using the <code class="docutils literal notranslate"><span class="pre">classes=</span></code> parameter.</p>
<p>Another aspect to consider when choosing a proper algorithm is that not all of
them put the same importance on each example over time. Namely, the
<code class="docutils literal notranslate"><span class="pre">Perceptron</span></code> is still sensitive to badly labeled examples even after many
examples whereas the <code class="docutils literal notranslate"><span class="pre">SGD*</span></code> and <code class="docutils literal notranslate"><span class="pre">PassiveAggressive*</span></code> families are more
robust to this kind of artifacts. Conversely, the latter also tend to give less
importance to remarkably different, yet properly labeled examples when they
come late in the stream as their learning rate decreases over time.</p>
</section>
<section id="examples">
<h3><span class="section-number">8.1.1.4. </span>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h3>
<p>Finally, we have a full-fledged example of
<a class="reference internal" href="../auto_examples/applications/plot_out_of_core_classification.html#sphx-glr-auto-examples-applications-plot-out-of-core-classification-py"><span class="std std-ref">Out-of-core classification of text documents</span></a>. It is aimed at
providing a starting point for people wanting to build out-of-core learning
systems and demonstrates most of the notions discussed above.</p>
<p>Furthermore, it also shows the evolution of the performance of different
algorithms with the number of processed examples.</p>
<p class="centered">
<strong><a class="reference external" href="../auto_examples/applications/plot_out_of_core_classification.html"><img alt="accuracy_over_time" src="../_images/sphx_glr_plot_out_of_core_classification_001.png" style="width: 512.0px; height: 384.0px;" /></a></strong></p><p>Now looking at the computation time of the different parts, we see that the
vectorization is much more expensive than learning itself. From the different
algorithms, <code class="docutils literal notranslate"><span class="pre">MultinomialNB</span></code> is the most expensive, but its overhead can be
mitigated by increasing the size of the mini-batches (exercise: change
<code class="docutils literal notranslate"><span class="pre">minibatch_size</span></code> to 100 and 10000 in the program and compare).</p>
<p class="centered">
<strong><a class="reference external" href="../auto_examples/applications/plot_out_of_core_classification.html"><img alt="computation_time" src="../_images/sphx_glr_plot_out_of_core_classification_003.png" style="width: 512.0px; height: 384.0px;" /></a></strong></p></section>
<section id="notes">
<h3><span class="section-number">8.1.1.5. </span>Notes<a class="headerlink" href="#notes" title="Link to this heading">#</a></h3>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id2" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
<p>Depending on the algorithm the mini-batch size can influence results or
not. SGD*, PassiveAggressive*, and discrete NaiveBayes are truly online
and are not affected by batch size. Conversely, MiniBatchKMeans
convergence rate is affected by the batch size. Also, its memory
footprint can vary dramatically with batch size.</p>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#scaling-with-instances-using-out-of-core-learning">8.1.1. Scaling with instances using out-of-core learning</a><ul class="nav section-nav flex-column">
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