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<section id="train-test-split">
<h1>train_test_split<a class="headerlink" href="#train-test-split" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.model_selection.train_test_split">
<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">train_test_split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stratify</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_split.py#L2708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.train_test_split" title="Link to this definition">#</a></dt>
<dd><p>Split arrays or matrices into random train and test subsets.</p>
<p>Quick utility that wraps input validation,
<code class="docutils literal notranslate"><span class="pre">next(ShuffleSplit().split(X,</span> <span class="pre">y))</span></code>, and application to input data
into a single call for splitting (and optionally subsampling) data into a
one-liner.</p>
<p>Read more in the <a class="reference internal" href="../cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>*arrays</strong><span class="classifier">sequence of indexables with same length / shape[0]</span></dt><dd><p>Allowed inputs are lists, numpy arrays, scipy-sparse
matrices or pandas dataframes.</p>
</dd>
<dt><strong>test_size</strong><span class="classifier">float or int, default=None</span></dt><dd><p>If float, should be between 0.0 and 1.0 and represent the proportion
of the dataset to include in the test split. If int, represents the
absolute number of test samples. If None, the value is set to the
complement of the train size. If <code class="docutils literal notranslate"><span class="pre">train_size</span></code> is also None, it will
be set to 0.25.</p>
</dd>
<dt><strong>train_size</strong><span class="classifier">float or int, default=None</span></dt><dd><p>If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the train split. If
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the shuffling applied to the data before applying the split.
Pass an int for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether or not to shuffle the data before splitting. If shuffle=False
then stratify must be None.</p>
</dd>
<dt><strong>stratify</strong><span class="classifier">array-like, default=None</span></dt><dd><p>If not None, data is split in a stratified fashion, using this as
the class labels.
Read more in the <a class="reference internal" href="../cross_validation.html#stratification"><span class="std std-ref">User Guide</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>splitting</strong><span class="classifier">list, length=2 * len(arrays)</span></dt><dd><p>List containing train-test split of inputs.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.16: </span>If the input is sparse, the output will be a
<code class="docutils literal notranslate"><span class="pre">scipy.sparse.csr_matrix</span></code>. Else, output type is the same as the
input type.</p>
</div>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest 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="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">X</span><span class="p">,</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="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span>
<span class="go">array([[0, 1],</span>
<span class="go"> [2, 3],</span>
<span class="go"> [4, 5],</span>
<span class="go"> [6, 7],</span>
<span class="go"> [8, 9]])</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="go">[0, 1, 2, 3, 4]</span>
</pre></div>
</div>
<div class="doctest 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">test_size</span><span class="o">=</span><span class="mf">0.33</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="gp">>>> </span><span class="n">X_train</span>
<span class="go">array([[4, 5],</span>
<span class="go"> [0, 1],</span>
<span class="go"> [6, 7]])</span>
<span class="gp">>>> </span><span class="n">y_train</span>
<span class="go">[2, 0, 3]</span>
<span class="gp">>>> </span><span class="n">X_test</span>
<span class="go">array([[2, 3],</span>
<span class="go"> [8, 9]])</span>
<span class="gp">>>> </span><span class="n">y_test</span>
<span class="go">[1, 4]</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">train_test_split</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">[[0, 1, 2], [3, 4]]</span>
</pre></div>
</div>
</dd></dl>
<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.5! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exhaustive list of all the changes, please refer to the release notes <release_notes_1_5>."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_1_5_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_1_5_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-5-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.5</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.5</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.4! Many bug fixes and improvements were added, as well as some new key features. We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes <release_notes_1_4>."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_1_4_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_1_4_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-4-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.4</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.4</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.24! Many bug fixes and improvements were added, as well as some new key features. We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes <release_notes_0_24>."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_0_24_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_0_24_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-24-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.24</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.24</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.23! Many bug fixes and improvements were added, as well as some new key features. We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes <release_notes_0_23>."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_0_23_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_0_23_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-23-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.23</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.23</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes and new features! We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes <release_notes_0_22>."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.22</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.22</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Well calibrated classifiers are probabilistic classifiers for which the output of predict_proba can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that for the samples to which it gave a predict_proba value close to 0.8, approximately 80% actually belong to the positive class."><img alt="" src="../../_images/sphx_glr_plot_compare_calibration_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py"><span class="std std-ref">Comparison of Calibration of Classifiers</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison of Calibration of Classifiers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence on the prediction. This example demonstrates how to visualize how well calibrated the predicted probabilities are using calibration curves, also known as reliability diagrams. Calibration of an uncalibrated classifier will also be demonstrated."><img alt="" src="../../_images/sphx_glr_plot_calibration_curve_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/calibration/plot_calibration_curve.html#sphx-glr-auto-examples-calibration-plot-calibration-curve-py"><span class="std std-ref">Probability Calibration curves</span></a></p>
<div class="sphx-glr-thumbnail-title">Probability Calibration curves</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some kind of confidence on the prediction. However, not all classifiers provide well-calibrated probabilities, some being over-confident while others being under-confident. Thus, a separate calibration of predicted probabilities is often desirable as a postprocessing. This example illustrates two different methods for this calibration and evaluates the quality of the returned probabilities using Brier's score (see https://en.wikipedia.org/wiki/Brier_score)."><img alt="" src="../../_images/sphx_glr_plot_calibration_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/calibration/plot_calibration.html#sphx-glr-auto-examples-calibration-plot-calibration-py"><span class="std std-ref">Probability calibration of classifiers</span></a></p>
<div class="sphx-glr-thumbnail-title">Probability calibration of classifiers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets."><img alt="" src="../../_images/sphx_glr_plot_classifier_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</span></a></p>
<div class="sphx-glr-thumbnail-title">Classifier comparison</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how scikit-learn can be used to recognize images of hand-written digits, from 0-9."><img alt="" src="../../_images/sphx_glr_plot_digits_classification_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py"><span class="std std-ref">Recognizing hand-written digits</span></a></p>
<div class="sphx-glr-thumbnail-title">Recognizing hand-written digits</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares Principal Component Regression (PCR) and Partial Least Squares Regression (PLS) on a toy dataset. Our goal is to illustrate how PLS can outperform PCR when the target is strongly correlated with some directions in the data that have a low variance."><img alt="" src="../../_images/sphx_glr_plot_pcr_vs_pls_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cross_decomposition/plot_pcr_vs_pls.html#sphx-glr-auto-examples-cross-decomposition-plot-pcr-vs-pls-py"><span class="std std-ref">Principal Component Regression vs Partial Least Squares Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Principal Component Regression vs Partial Least Squares Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to prevent a tree from overfiting. Cost complexity pruning provides another option to control the size of a tree. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. Greater values of ccp_alpha increase the number of nodes pruned. Here we only show the effect of ccp_alpha on regularizing the trees and how to choose a ccp_alpha based on validation scores."><img alt="" src="../../_images/sphx_glr_plot_cost_complexity_pruning_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py"><span class="std std-ref">Post pruning decision trees with cost complexity pruning</span></a></p>
<div class="sphx-glr-thumbnail-title">Post pruning decision trees with cost complexity pruning</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The decision tree structure can be analysed to gain further insight on the relation between the features and the target to predict. In this example, we show how to retrieve:"><img alt="" src="../../_images/sphx_glr_plot_unveil_tree_structure_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py"><span class="std std-ref">Understanding the decision tree structure</span></a></p>
<div class="sphx-glr-thumbnail-title">Understanding the decision tree structure</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the difference between the Principal Components Analysis (~sklearn.decomposition.PCA) and its kernelized version (~sklearn.decomposition.KernelPCA)."><img alt="" src="../../_images/sphx_glr_plot_kernel_pca_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py"><span class="std std-ref">Kernel PCA</span></a></p>
<div class="sphx-glr-thumbnail-title">Kernel PCA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example to compare multi-output regression with random forest and the multiclass meta-estimator."><img alt="" src="../../_images/sphx_glr_plot_random_forest_regression_multioutput_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py"><span class="std std-ref">Comparing random forests and the multi-output meta estimator</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing random forests and the multi-output meta estimator</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient Boosting is an ensemble technique that combines multiple weak learners, typically decision trees, to create a robust and powerful predictive model. It does so in an iterative fashion, where each new stage (tree) corrects the errors of the previous ones."><img alt="" src="../../_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py"><span class="std std-ref">Early stopping in Gradient Boosting</span></a></p>
<div class="sphx-glr-thumbnail-title">Early stopping in Gradient Boosting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of a forest of trees to evaluate the importance of features on an artificial classification task. The blue bars are the feature importances of the forest, along with their inter-trees variability represented by the error bars."><img alt="" src="../../_images/sphx_glr_plot_forest_importances_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_forest_importances.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-py"><span class="std std-ref">Feature importances with a forest of trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Feature importances with a forest of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on these features."><img alt="" src="../../_images/sphx_glr_plot_feature_transformation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Feature transformations with ensembles of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="histogram_based_gradient_boosting (HGBT) models may be one of the most useful supervised learning models in scikit-learn. They are based on a modern gradient boosting implementation comparable to LightGBM and XGBoost. As such, HGBT models are more feature rich than and often outperform alternative models like random forests, especially when the number of samples is larger than some ten thousands (see sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py)."><img alt="" src="../../_images/sphx_glr_plot_hgbt_regression_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_hgbt_regression.html#sphx-glr-auto-examples-ensemble-plot-hgbt-regression-py"><span class="std std-ref">Features in Histogram Gradient Boosting Trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Features in Histogram Gradient Boosting Trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient Boosting Out-of-Bag estimates"><img alt="" src="../../_images/sphx_glr_plot_gradient_boosting_oob_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py"><span class="std std-ref">Gradient Boosting Out-of-Bag estimates</span></a></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting Out-of-Bag estimates</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of weak predictive models. Gradient boosting can be used for regression and classification problems. Here, we will train a model to tackle a diabetes regression task. We will obtain the results from GradientBoostingRegressor with least squares loss and 500 regression trees of depth 4."><img alt="" src="../../_images/sphx_glr_plot_gradient_boosting_regression_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py"><span class="std std-ref">Gradient Boosting regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Illustration of the effect of different regularization strategies for Gradient Boosting. The example is taken from Hastie et al 2009 [1]_."><img alt="" src="../../_images/sphx_glr_plot_gradient_boosting_regularization_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_regularization.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regularization-py"><span class="std std-ref">Gradient Boosting regularization</span></a></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting regularization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example using IsolationForest for anomaly detection."><img alt="" src="../../_images/sphx_glr_plot_isolation_forest_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py"><span class="std std-ref">IsolationForest example</span></a></p>
<div class="sphx-glr-thumbnail-title">IsolationForest example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how boosting can improve the prediction accuracy on a multi-label classification problem. It reproduces a similar experiment as depicted by Figure 1 in Zhu et al [1]_."><img alt="" src="../../_images/sphx_glr_plot_adaboost_multiclass_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py"><span class="std std-ref">Multi-class AdaBoosted Decision Trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Multi-class AdaBoosted Decision Trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how quantile regression can be used to create prediction intervals. See sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py for an example showcasing some other features of HistGradientBoostingRegressor."><img alt="" src="../../_images/sphx_glr_plot_gradient_boosting_quantile_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_quantile.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-quantile-py"><span class="std std-ref">Prediction Intervals for Gradient Boosting Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Prediction Intervals for Gradient Boosting Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", aka LFW_: http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)"><img alt="" src="../../_images/sphx_glr_plot_face_recognition_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py"><span class="std std-ref">Faces recognition example using eigenfaces and SVMs</span></a></p>
<div class="sphx-glr-thumbnail-title">Faces recognition example using eigenfaces and SVMs</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use KernelPCA to denoise images. In short, we take advantage of the approximation function learned during fit to reconstruct the original image."><img alt="" src="../../_images/sphx_glr_plot_digits_denoising_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_digits_denoising.html#sphx-glr-auto-examples-applications-plot-digits-denoising-py"><span class="std std-ref">Image denoising using kernel PCA</span></a></p>
<div class="sphx-glr-thumbnail-title">Image denoising using kernel PCA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how Polars-engineered lagged features can be used for time series forecasting with HistGradientBoostingRegressor on the Bike Sharing Demand dataset."><img alt="" src="../../_images/sphx_glr_plot_time_series_lagged_features_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_time_series_lagged_features.html#sphx-glr-auto-examples-applications-plot-time-series-lagged-features-py"><span class="std std-ref">Lagged features for time series forecasting</span></a></p>
<div class="sphx-glr-thumbnail-title">Lagged features for time series forecasting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrate how model complexity influences both prediction accuracy and computational performance."><img alt="" src="../../_images/sphx_glr_plot_model_complexity_influence_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_model_complexity_influence.html#sphx-glr-auto-examples-applications-plot-model-complexity-influence-py"><span class="std std-ref">Model Complexity Influence</span></a></p>
<div class="sphx-glr-thumbnail-title">Model Complexity Influence</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing the prediction latency of various scikit-learn estimators."><img alt="" src="../../_images/sphx_glr_plot_prediction_latency_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_prediction_latency.html#sphx-glr-auto-examples-applications-plot-prediction-latency-py"><span class="std std-ref">Prediction Latency</span></a></p>
<div class="sphx-glr-thumbnail-title">Prediction Latency</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how a feature selection can be easily integrated within a machine learning pipeline."><img alt="" src="../../_images/sphx_glr_plot_feature_selection_pipeline_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py"><span class="std std-ref">Pipeline ANOVA SVM</span></a></p>
<div class="sphx-glr-thumbnail-title">Pipeline ANOVA SVM</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This notebook is an example of using univariate feature selection to improve classification accuracy on a noisy dataset."><img alt="" src="../../_images/sphx_glr_plot_feature_selection_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/feature_selection/plot_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py"><span class="std std-ref">Univariate Feature Selection</span></a></p>
<div class="sphx-glr-thumbnail-title">Univariate Feature Selection</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparing various online solvers"><img alt="" src="../../_images/sphx_glr_plot_sgd_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_sgd_comparison.html#sphx-glr-auto-examples-linear-model-plot-sgd-comparison-py"><span class="std std-ref">Comparing various online solvers</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing various online solvers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Stochastic Gradient Descent is an optimization technique which minimizes a loss function in a stochastic fashion, performing a gradient descent step sample by sample. In particular, it is a very efficient method to fit linear models."><img alt="" src="../../_images/sphx_glr_plot_sgd_early_stopping_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_sgd_early_stopping.html#sphx-glr-auto-examples-linear-model-plot-sgd-early-stopping-py"><span class="std std-ref">Early stopping of Stochastic Gradient Descent</span></a></p>
<div class="sphx-glr-thumbnail-title">Early stopping of Stochastic Gradient Descent</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The present example compares three l1-based regression models on a synthetic signal obtained from sparse and correlated features that are further corrupted with additive gaussian noise:"><img alt="" src="../../_images/sphx_glr_plot_lasso_and_elasticnet_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py"><span class="std std-ref">L1-based models for Sparse Signals</span></a></p>
<div class="sphx-glr-thumbnail-title">L1-based models for Sparse Signals</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits classification task. We use the SAGA algorithm for this purpose: this a solver that is fast when the number of samples is significantly larger than the number of features and is able to finely optimize non-smooth objective functions which is the case with the l1-penalty. Test accuracy reaches > 0.8, while weight vectors remains sparse and therefore more easily interpretable."><img alt="" src="../../_images/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py"><span class="std std-ref">MNIST classification using multinomial logistic + L1</span></a></p>
<div class="sphx-glr-thumbnail-title">MNIST classification using multinomial logistic + L1</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of multinomial logistic L1 vs one-versus-rest L1 logistic regression to classify documents from the newgroups20 dataset. Multinomial logistic regression yields more accurate results and is faster to train on the larger scale dataset."><img alt="" src="../../_images/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py"><span class="std std-ref">Multiclass sparse logistic regression on 20newgroups</span></a></p>
<div class="sphx-glr-thumbnail-title">Multiclass sparse logistic regression on 20newgroups</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we fit a linear model with positive constraints on the regression coefficients and compare the estimated coefficients to a classic linear regression."><img alt="" src="../../_images/sphx_glr_plot_nnls_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_nnls.html#sphx-glr-auto-examples-linear-model-plot-nnls-py"><span class="std std-ref">Non-negative least squares</span></a></p>
<div class="sphx-glr-thumbnail-title">Non-negative least squares</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the ordinary least squares (OLS) model called LinearRegression in scikit-learn."><img alt="" src="../../_images/sphx_glr_plot_ols_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py"><span class="std std-ref">Ordinary Least Squares Example</span></a></p>
<div class="sphx-glr-thumbnail-title">Ordinary Least Squares Example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of log-linear Poisson regression on the French Motor Third-Party Liability Claims dataset from [1]_ and compares it with a linear model fitted with the usual least squared error and a non-linear GBRT model fitted with the Poisson loss (and a log-link)."><img alt="" src="../../_images/sphx_glr_plot_poisson_regression_non_normal_loss_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html#sphx-glr-auto-examples-linear-model-plot-poisson-regression-non-normal-loss-py"><span class="std std-ref">Poisson regression and non-normal loss</span></a></p>
<div class="sphx-glr-thumbnail-title">Poisson regression and non-normal loss</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of Poisson, Gamma and Tweedie regression on the French Motor Third-Party Liability Claims dataset, and is inspired by an R tutorial [1]_."><img alt="" src="../../_images/sphx_glr_plot_tweedie_regression_insurance_claims_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_tweedie_regression_insurance_claims.html#sphx-glr-auto-examples-linear-model-plot-tweedie-regression-insurance-claims-py"><span class="std std-ref">Tweedie regression on insurance claims</span></a></p>
<div class="sphx-glr-thumbnail-title">Tweedie regression on insurance claims</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In linear models, the target value is modeled as a linear combination of the features (see the linear_model User Guide section for a description of a set of linear models available in scikit-learn). Coefficients in multiple linear models represent the relationship between the given feature, X_i and the target, y, assuming that all the other features remain constant (conditional dependence). This is different from plotting X_i versus y and fitting a linear relationship: in that case all possible values of the other features are taken into account in the estimation (marginal dependence)."><img alt="" src="../../_images/sphx_glr_plot_linear_model_coefficient_interpretation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py"><span class="std std-ref">Common pitfalls in the interpretation of coefficients of linear models</span></a></p>
<div class="sphx-glr-thumbnail-title">Common pitfalls in the interpretation of coefficients of linear models</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Machine Learning models are great for measuring statistical associations. Unfortunately, unless we're willing to make strong assumptions about the data, those models are unable to infer causal effects."><img alt="" src="../../_images/sphx_glr_plot_causal_interpretation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_causal_interpretation.html#sphx-glr-auto-examples-inspection-plot-causal-interpretation-py"><span class="std std-ref">Failure of Machine Learning to infer causal effects</span></a></p>
<div class="sphx-glr-thumbnail-title">Failure of Machine Learning to infer causal effects</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the impurity-based feature importance of RandomForestClassifier with the permutation importance on the titanic dataset using permutation_importance. We will show that the impurity-based feature importance can inflate the importance of numerical features."><img alt="" src="../../_images/sphx_glr_plot_permutation_importance_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py"><span class="std std-ref">Permutation Importance vs Random Forest Feature Importance (MDI)</span></a></p>
<div class="sphx-glr-thumbnail-title">Permutation Importance vs Random Forest Feature Importance (MDI)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compute the permutation_importance of the features to a trained RandomForestClassifier using the breast_cancer_dataset. The model can easily get about 97% accuracy on a test dataset. Because this dataset contains multicollinear features, the permutation importance shows that none of the features are important, in contradiction with the high test accuracy."><img alt="" src="../../_images/sphx_glr_plot_permutation_importance_multicollinear_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_permutation_importance_multicollinear.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-multicollinear-py"><span class="std std-ref">Permutation Importance with Multicollinear or Correlated Features</span></a></p>
<div class="sphx-glr-thumbnail-title">Permutation Importance with Multicollinear or Correlated Features</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of PolynomialCountSketch to efficiently generate polynomial kernel feature-space approximations. This is used to train linear classifiers that approximate the accuracy of kernelized ones."><img alt="" src="../../_images/sphx_glr_plot_scalable_poly_kernels_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/kernel_approximation/plot_scalable_poly_kernels.html#sphx-glr-auto-examples-kernel-approximation-plot-scalable-poly-kernels-py"><span class="std std-ref">Scalable learning with polynomial kernel approximation</span></a></p>
<div class="sphx-glr-thumbnail-title">Scalable learning with polynomial kernel approximation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares two outlier detection algorithms, namely local_outlier_factor (LOF) and isolation_forest (IForest), on real-world datasets available in sklearn.datasets. The goal is to show that different algorithms perform well on different datasets and contrast their training speed and sensitivity to hyperparameters."><img alt="" src="../../_images/sphx_glr_plot_outlier_detection_bench_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_outlier_detection_bench.html#sphx-glr-auto-examples-miscellaneous-plot-outlier-detection-bench-py"><span class="std std-ref">Evaluation of outlier detection estimators</span></a></p>
<div class="sphx-glr-thumbnail-title">Evaluation of outlier detection estimators</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example will demonstrate the set_output API to configure transformers to output pandas DataFrames. set_output can be configured per estimator by calling the set_output method or globally by setting set_config(transform_output="pandas"). For details, see SLEP018."><img alt="" src="../../_images/sphx_glr_plot_set_output_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a></p>
<div class="sphx-glr-thumbnail-title">Introducing the set_output API</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="ROC Curve with Visualization API"><img alt="" src="../../_images/sphx_glr_plot_roc_curve_visualization_api_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a></p>
<div class="sphx-glr-thumbnail-title">ROC Curve with Visualization API</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will construct display objects, ConfusionMatrixDisplay, RocCurveDisplay, and PrecisionRecallDisplay directly from their respective metrics. This is an alternative to using their corresponding plot functions when a model's predictions are already computed or expensive to compute. Note that this is advanced usage, and in general we recommend using their respective plot functions."><img alt="" src="../../_images/sphx_glr_plot_display_object_visualization_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_display_object_visualization.html#sphx-glr-auto-examples-miscellaneous-plot-display-object-visualization-py"><span class="std std-ref">Visualizations with Display Objects</span></a></p>
<div class="sphx-glr-thumbnail-title">Visualizations with Display Objects</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the class_likelihood_ratios function, which computes the positive and negative likelihood ratios (`LR+`, LR-) to assess the predictive power of a binary classifier. As we will see, these metrics are independent of the proportion between classes in the test set, which makes them very useful when the available data for a study has a different class proportion than the target application."><img alt="" src="../../_images/sphx_glr_plot_likelihood_ratios_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_likelihood_ratios.html#sphx-glr-auto-examples-model-selection-plot-likelihood-ratios-py"><span class="std std-ref">Class Likelihood Ratios to measure classification performance</span></a></p>
<div class="sphx-glr-thumbnail-title">Class Likelihood Ratios to measure classification performance</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. The higher the diagonal values of the confusion matrix the better, indicating many correct predictions."><img alt="" src="../../_images/sphx_glr_plot_confusion_matrix_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py"><span class="std std-ref">Confusion matrix</span></a></p>
<div class="sphx-glr-thumbnail-title">Confusion matrix</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This examples shows how a classifier is optimized by cross-validation, which is done using the GridSearchCV object on a development set that comprises only half of the available labeled data."><img alt="" src="../../_images/sphx_glr_plot_grid_search_digits_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py"><span class="std std-ref">Custom refit strategy of a grid search with cross-validation</span></a></p>
<div class="sphx-glr-thumbnail-title">Custom refit strategy of a grid search with cross-validation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare two binary classification multi-threshold metrics: the Receiver Operating Characteristic (ROC) and the Detection Error Tradeoff (DET). For such purpose, we evaluate two different classifiers for the same classification task."><img alt="" src="../../_images/sphx_glr_plot_det_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_det.html#sphx-glr-auto-examples-model-selection-plot-det-py"><span class="std std-ref">Detection error tradeoff (DET) curve</span></a></p>
<div class="sphx-glr-thumbnail-title">Detection error tradeoff (DET) curve</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we evaluate the impact of the regularization parameter in a linear model called ElasticNet. To carry out this evaluation, we use a validation curve using ValidationCurveDisplay. This curve shows the training and test scores of the model for different values of the regularization parameter."><img alt="" src="../../_images/sphx_glr_plot_train_error_vs_test_error_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_train_error_vs_test_error.html#sphx-glr-auto-examples-model-selection-plot-train-error-vs-test-error-py"><span class="std std-ref">Effect of model regularization on training and test error</span></a></p>
<div class="sphx-glr-thumbnail-title">Effect of model regularization on training and test error</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluate the quality of multiclass classifiers."><img alt="" src="../../_images/sphx_glr_plot_roc_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py"><span class="std std-ref">Multiclass Receiver Operating Characteristic (ROC)</span></a></p>
<div class="sphx-glr-thumbnail-title">Multiclass Receiver Operating Characteristic (ROC)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Once a classifier is trained, the output of the predict method outputs class label predictions corresponding to a thresholding of either the decision_function or the predict_proba output. For a binary classifier, the default threshold is defined as a posterior probability estimate of 0.5 or a decision score of 0.0."><img alt="" src="../../_images/sphx_glr_plot_cost_sensitive_learning_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_cost_sensitive_learning.html#sphx-glr-auto-examples-model-selection-plot-cost-sensitive-learning-py"><span class="std std-ref">Post-tuning the decision threshold for cost-sensitive learning</span></a></p>
<div class="sphx-glr-thumbnail-title">Post-tuning the decision threshold for cost-sensitive learning</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Example of Precision-Recall metric to evaluate classifier output quality."><img alt="" src="../../_images/sphx_glr_plot_precision_recall_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py"><span class="std std-ref">Precision-Recall</span></a></p>
<div class="sphx-glr-thumbnail-title">Precision-Recall</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The most naive strategy to solve such a task is to independently train a binary classifier on each label (i.e. each column of the target variable). At prediction time, the ensemble of binary classifiers is used to assemble multitask prediction."><img alt="" src="../../_images/sphx_glr_plot_classifier_chain_yeast_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/multioutput/plot_classifier_chain_yeast.html#sphx-glr-auto-examples-multioutput-plot-classifier-chain-yeast-py"><span class="std std-ref">Multilabel classification using a classifier chain</span></a></p>
<div class="sphx-glr-thumbnail-title">Multilabel classification using a classifier chain</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example comparing nearest neighbors classification with and without Neighborhood Components Analysis."><img alt="" src="../../_images/sphx_glr_plot_nca_classification_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py"><span class="std std-ref">Comparing Nearest Neighbors with and without Neighborhood Components Analysis</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing Nearest Neighbors with and without Neighborhood Components Analysis</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Sample usage of Neighborhood Components Analysis for dimensionality reduction."><img alt="" src="../../_images/sphx_glr_plot_nca_dim_reduction_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py"><span class="std std-ref">Dimensionality Reduction with Neighborhood Components Analysis</span></a></p>
<div class="sphx-glr-thumbnail-title">Dimensionality Reduction with Neighborhood Components Analysis</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use KNeighborsClassifier. We train such a classifier on the iris dataset and observe the difference of the decision boundary obtained with regards to the parameter weights."><img alt="" src="../../_images/sphx_glr_plot_classification_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py"><span class="std std-ref">Nearest Neighbors Classification</span></a></p>
<div class="sphx-glr-thumbnail-title">Nearest Neighbors Classification</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="For greyscale image data where pixel values can be interpreted as degrees of blackness on a white background, like handwritten digit recognition, the Bernoulli Restricted Boltzmann machine model (BernoulliRBM) can perform effective non-linear feature extraction."><img alt="" src="../../_images/sphx_glr_plot_rbm_logistic_classification_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neural_networks/plot_rbm_logistic_classification.html#sphx-glr-auto-examples-neural-networks-plot-rbm-logistic-classification-py"><span class="std std-ref">Restricted Boltzmann Machine features for digit classification</span></a></p>
<div class="sphx-glr-thumbnail-title">Restricted Boltzmann Machine features for digit classification</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A comparison of different values for regularization parameter 'alpha' on synthetic datasets. The plot shows that different alphas yield different decision functions."><img alt="" src="../../_images/sphx_glr_plot_mlp_alpha_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py"><span class="std std-ref">Varying regularization in Multi-layer Perceptron</span></a></p>
<div class="sphx-glr-thumbnail-title">Varying regularization in Multi-layer Perceptron</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Sometimes looking at the learned coefficients of a neural network can provide insight into the learning behavior. For example if weights look unstructured, maybe some were not used at all, or if very large coefficients exist, maybe regularization was too low or the learning rate too high."><img alt="" src="../../_images/sphx_glr_plot_mnist_filters_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py"><span class="std std-ref">Visualization of MLP weights on MNIST</span></a></p>
<div class="sphx-glr-thumbnail-title">Visualization of MLP weights on MNIST</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how to apply different preprocessing and feature extraction pipelines to different subsets of features, using ColumnTransformer. This is particularly handy for the case of datasets that contain heterogeneous data types, since we may want to scale the numeric features and one-hot encode the categorical ones."><img alt="" src="../../_images/sphx_glr_plot_column_transformer_mixed_types_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py"><span class="std std-ref">Column Transformer with Mixed Types</span></a></p>
<div class="sphx-glr-thumbnail-title">Column Transformer with Mixed Types</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we give an overview of TransformedTargetRegressor. We use two examples to illustrate the benefit of transforming the targets before learning a linear regression model. The first example uses synthetic data while the second example is based on the Ames housing data set."><img alt="" src="../../_images/sphx_glr_plot_transformed_target_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/compose/plot_transformed_target.html#sphx-glr-auto-examples-compose-plot-transformed-target-py"><span class="std std-ref">Effect of transforming the targets in regression model</span></a></p>
<div class="sphx-glr-thumbnail-title">Effect of transforming the targets in regression model</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A demonstration of feature discretization on synthetic classification datasets. Feature discretization decomposes each feature into a set of bins, here equally distributed in width. The discrete values are then one-hot encoded, and given to a linear classifier. This preprocessing enables a non-linear behavior even though the classifier is linear."><img alt="" src="../../_images/sphx_glr_plot_discretization_classification_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_discretization_classification.html#sphx-glr-auto-examples-preprocessing-plot-discretization-classification-py"><span class="std std-ref">Feature discretization</span></a></p>
<div class="sphx-glr-thumbnail-title">Feature discretization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Feature scaling through standardization, also called Z-score normalization, is an important preprocessing step for many machine learning algorithms. It involves rescaling each feature such that it has a standard deviation of 1 and a mean of 0."><img alt="" src="../../_images/sphx_glr_plot_scaling_importance_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_scaling_importance.html#sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py"><span class="std std-ref">Importance of Feature Scaling</span></a></p>
<div class="sphx-glr-thumbnail-title">Importance of Feature Scaling</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the use of the Box-Cox and Yeo-Johnson transforms through PowerTransformer to map data from various distributions to a normal distribution."><img alt="" src="../../_images/sphx_glr_plot_map_data_to_normal_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_map_data_to_normal.html#sphx-glr-auto-examples-preprocessing-plot-map-data-to-normal-py"><span class="std std-ref">Map data to a normal distribution</span></a></p>
<div class="sphx-glr-thumbnail-title">Map data to a normal distribution</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The TargetEncoder replaces each category of a categorical feature with the shrunk mean of the target variable for that category. This method is useful in cases where there is a strong relationship between the categorical feature and the target. To prevent overfitting, TargetEncoder.fit_transform uses an internal cross fitting scheme to encode the training data to be used by a downstream model. This scheme involves splitting the data into k folds and encoding each fold using the encodings learnt using the other k-1 folds. In this example, we demonstrate the importance of the cross fitting procedure to prevent overfitting."><img alt="" src="../../_images/sphx_glr_plot_target_encoder_cross_val_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_target_encoder_cross_val.html#sphx-glr-auto-examples-preprocessing-plot-target-encoder-cross-val-py"><span class="std std-ref">Target Encoder’s Internal Cross fitting</span></a></p>
<div class="sphx-glr-thumbnail-title">Target Encoder's Internal Cross fitting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, semi-supervised classifiers are trained on the 20 newsgroups dataset (which will be automatically downloaded)."><img alt="" src="../../_images/sphx_glr_plot_semi_supervised_newsgroups_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/semi_supervised/plot_semi_supervised_newsgroups.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-newsgroups-py"><span class="std std-ref">Semi-supervised Classification on a Text Dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Semi-supervised Classification on a Text Dataset</div>
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