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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.load_iris</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.load_iris"><code class="docutils literal notranslate"><span class="pre">load_iris</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-load-iris">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_iris</span></code></a></li>
</ul>
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<section id="sklearn-datasets-load-iris">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>.load_iris<a class="headerlink" href="#sklearn-datasets-load-iris" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.load_iris">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_iris</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/datasets/_base.py#L580"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_iris" title="Permalink to this definition">¶</a></dt>
<dd><p>Load and return the iris dataset (classification).</p>
<p>The iris dataset is a classic and very easy multi-class classification
dataset.</p>
<table class="docutils align-default">
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>3</p></td>
</tr>
<tr class="row-even"><td><p>Samples per class</p></td>
<td><p>50</p></td>
</tr>
<tr class="row-odd"><td><p>Samples total</p></td>
<td><p>150</p></td>
</tr>
<tr class="row-even"><td><p>Dimensionality</p></td>
<td><p>4</p></td>
</tr>
<tr class="row-odd"><td><p>Features</p></td>
<td><p>real, positive</p></td>
</tr>
</tbody>
</table>
<p>Read more in the <a class="reference internal" href="../../datasets/toy_dataset.html#iris-dataset"><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>
<dt><strong>return_X_y</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, returns <code class="docutils literal notranslate"><span class="pre">(data,</span> <span class="pre">target)</span></code> instead of a Bunch object. See
below for more information about the <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">target</span></code> object.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</span></p>
</div>
</dd>
<dt><strong>as_frame</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric). The target is
a pandas DataFrame or Series depending on the number of target columns.
If <code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> is True, then (<code class="docutils literal notranslate"><span class="pre">data</span></code>, <code class="docutils literal notranslate"><span class="pre">target</span></code>) will be pandas
DataFrames or Series as described below.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.23.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>data</strong><span class="classifier"><a class="reference internal" href="sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch"><code class="xref py py-class docutils literal notranslate"><span class="pre">Bunch</span></code></a></span></dt><dd><p>Dictionary-like object, with the following attributes.</p>
<dl>
<dt>data<span class="classifier">{ndarray, dataframe} of shape (150, 4)</span></dt><dd><p>The data matrix. If <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>, <code class="docutils literal notranslate"><span class="pre">data</span></code> will be a pandas
DataFrame.</p>
</dd>
<dt>target: {ndarray, Series} of shape (150,)</dt><dd><p>The classification target. If <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>, <code class="docutils literal notranslate"><span class="pre">target</span></code> will be
a pandas Series.</p>
</dd>
<dt>feature_names: list</dt><dd><p>The names of the dataset columns.</p>
</dd>
<dt>target_names: list</dt><dd><p>The names of target classes.</p>
</dd>
<dt>frame: DataFrame of shape (150, 5)</dt><dd><p>Only present when <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>. DataFrame with <code class="docutils literal notranslate"><span class="pre">data</span></code> and
<code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.23.</span></p>
</div>
</dd>
<dt>DESCR: str</dt><dd><p>The full description of the dataset.</p>
</dd>
<dt>filename: str</dt><dd><p>The path to the location of the data.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
</dl>
</dd>
<dt><strong>(data, target)</strong><span class="classifier">tuple if <code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> is True</span></dt><dd><p>A tuple of two ndarray. The first containing a 2D array of shape
(n_samples, n_features) with each row representing one sample and
each column representing the features. The second ndarray of shape
(n_samples,) containing the target samples.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.20: </span>Fixed two wrong data points according to Fisher’s paper.
The new version is the same as in R, but not as in the UCI
Machine Learning Repository.</p>
</div>
<p class="rubric">Examples</p>
<p>Let’s say you are interested in the samples 10, 25, and 50, and want to
know their class name.</p>
<div class="doctest 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">load_iris</span>
<span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">data</span><span class="o">.</span><span class="n">target</span><span class="p">[[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">50</span><span class="p">]]</span>
<span class="go">array([0, 0, 1])</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">target_names</span><span class="p">)</span>
<span class="go">['setosa', 'versicolor', 'virginica']</span>
</pre></div>
</div>
<p>See <a class="reference internal" href="../../auto_examples/datasets/plot_iris_dataset.html#sphx-glr-auto-examples-datasets-plot-iris-dataset-py"><span class="std std-ref">The Iris Dataset</span></a> for a more
detailed example of how to work with the iris dataset.</p>
</dd></dl>
<section id="examples-using-sklearn-datasets-load-iris">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_iris</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-load-iris" title="Permalink 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 1.2! Many bug fixes and improvements wer..."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_1_2_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_1_2_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-2-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.2</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.2</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 we..."><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 0.22, which comes with many bug fixes an..."><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="Plot the classification probability for different classifiers. We use a 3 class dataset, and we..."><img alt="" src="../../_images/sphx_glr_plot_classification_probability_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot classification probability</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The plot shows:"><img alt="" src="../../_images/sphx_glr_plot_cluster_iris_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_cluster_iris.html#sphx-glr-auto-examples-cluster-plot-cluster-iris-py"><span class="std std-ref">K-means Clustering</span></a></p>
<div class="sphx-glr-thumbnail-title">K-means Clustering</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot Hierarchical Clustering Dendrogram"><img alt="" src="../../_images/sphx_glr_plot_agglomerative_dendrogram_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py"><span class="std std-ref">Plot Hierarchical Clustering Dendrogram</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot Hierarchical Clustering Dendrogram</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and P..."><img alt="" src="../../_images/sphx_glr_plot_iris_dataset_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/datasets/plot_iris_dataset.html#sphx-glr-auto-examples-datasets-plot-iris-dataset-py"><span class="std std-ref">The Iris Dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">The Iris Dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surface of a decision tree trained on pairs of features of the iris dataset."><img alt="" src="../../_images/sphx_glr_plot_iris_dtc_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py"><span class="std std-ref">Plot the decision surface of decision trees trained on the iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot the decision surface of decision trees trained on the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The decision tree structure can be analysed to gain further insight on the relation between the..."><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="The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 a..."><img alt="" src="../../_images/sphx_glr_plot_pca_vs_lda_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py"><span class="std std-ref">Comparison of LDA and PCA 2D projection of Iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison of LDA and PCA 2D projection of Iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Investigating the Iris dataset, we see that sepal length, petal length and petal width are high..."><img alt="" src="../../_images/sphx_glr_plot_varimax_fa_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/decomposition/plot_varimax_fa.html#sphx-glr-auto-examples-decomposition-plot-varimax-fa-py"><span class="std std-ref">Factor Analysis (with rotation) to visualize patterns</span></a></p>
<div class="sphx-glr-thumbnail-title">Factor Analysis (with rotation) to visualize patterns</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Incremental principal component analysis (IPCA) is typically used as a replacement for principa..."><img alt="" src="../../_images/sphx_glr_plot_incremental_pca_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/decomposition/plot_incremental_pca.html#sphx-glr-auto-examples-decomposition-plot-incremental-pca-py"><span class="std std-ref">Incremental PCA</span></a></p>
<div class="sphx-glr-thumbnail-title">Incremental PCA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Principal Component Analysis applied to the Iris dataset."><img alt="" src="../../_images/sphx_glr_plot_pca_iris_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">PCA example with Iris Data-set</span></a></p>
<div class="sphx-glr-thumbnail-title">PCA example with Iris Data-set</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient boosting is an ensembling technique where several weak learners (regression trees) are..."><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 of Gradient Boosting</span></a></p>
<div class="sphx-glr-thumbnail-title">Early stopping of Gradient Boosting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="../../_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the i..."><img alt="" src="../../_images/sphx_glr_plot_forest_iris_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot the decision surfaces of ensembles of trees on the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This notebook is an example of using univariate feature selection to improve classification acc..."><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="Demonstration of several covariances types for Gaussian mixture models."><img alt="" src="../../_images/sphx_glr_plot_gmm_covariances_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/mixture/plot_gmm_covariances.html#sphx-glr-auto-examples-mixture-plot-gmm-covariances-py"><span class="std std-ref">GMM covariances</span></a></p>
<div class="sphx-glr-thumbnail-title">GMM covariances</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF ..."><img alt="" src="../../_images/sphx_glr_plot_gpc_iris_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/gaussian_process/plot_gpc_iris.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-iris-py"><span class="std std-ref">Gaussian process classification (GPC) on iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Gaussian process classification (GPC) on iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Show below is a logistic-regression classifiers decision boundaries on the first two dimensions..."><img alt="" src="../../_images/sphx_glr_plot_iris_logistic_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_iris_logistic.html#sphx-glr-auto-examples-linear-model-plot-iris-logistic-py"><span class="std std-ref">Logistic Regression 3-class Classifier</span></a></p>
<div class="sphx-glr-thumbnail-title">Logistic Regression 3-class Classifier</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the ..."><img alt="" src="../../_images/sphx_glr_plot_sgd_iris_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_sgd_iris.html#sphx-glr-auto-examples-linear-model-plot-sgd-iris-py"><span class="std std-ref">Plot multi-class SGD on the iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot multi-class SGD on the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=" Train l1-penalized logistic regression models on a binary classification problem derived from ..."><img alt="" src="../../_images/sphx_glr_plot_logistic_path_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_logistic_path.html#sphx-glr-auto-examples-linear-model-plot-logistic-path-py"><span class="std std-ref">Regularization path of L1- Logistic Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Regularization path of L1- Logistic Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example will demonstrate the set_output API to configure transformers to output pandas Dat..."><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="Example of confusion matrix usage to evaluate the quality of the output of a classifier on the ..."><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 example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluat..."><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="This example compares non-nested and nested cross-validation strategies on a classifier of the ..."><img alt="" src="../../_images/sphx_glr_plot_nested_cross_validation_iris_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a></p>
<div class="sphx-glr-thumbnail-title">Nested versus non-nested cross-validation</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="This example presents how to estimate and visualize the variance of the Receiver Operating Char..."><img alt="" src="../../_images/sphx_glr_plot_roc_crossval_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py"><span class="std std-ref">Receiver Operating Characteristic (ROC) with cross validation</span></a></p>
<div class="sphx-glr-thumbnail-title">Receiver Operating Characteristic (ROC) with cross validation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the use of permutation_test_score to evaluate the significance of a c..."><img alt="" src="../../_images/sphx_glr_plot_permutation_tests_for_classification_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_permutation_tests_for_classification.html#sphx-glr-auto-examples-model-selection-plot-permutation-tests-for-classification-py"><span class="std std-ref">Test with permutations the significance of a classification score</span></a></p>
<div class="sphx-glr-thumbnail-title">Test with permutations the significance of a classification score</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example comparing nearest neighbors classification with and without Neighborhood Components ..."><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 Nearest Centroid classification. It will plot the decision boundaries for each ..."><img alt="" src="../../_images/sphx_glr_plot_nearest_centroid_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neighbors/plot_nearest_centroid.html#sphx-glr-auto-examples-neighbors-plot-nearest-centroid-py"><span class="std std-ref">Nearest Centroid Classification</span></a></p>
<div class="sphx-glr-thumbnail-title">Nearest Centroid Classification</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use KNeighborsClassifier. We train such a classifier on the iris data..."><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="This example visualizes some training loss curves for different stochastic learning strategies,..."><img alt="" src="../../_images/sphx_glr_plot_mlp_training_curves_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neural_networks/plot_mlp_training_curves.html#sphx-glr-auto-examples-neural-networks-plot-mlp-training-curves-py"><span class="std std-ref">Compare Stochastic learning strategies for MLPClassifier</span></a></p>
<div class="sphx-glr-thumbnail-title">Compare Stochastic learning strategies for MLPClassifier</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In many real-world examples, there are many ways to extract features from a dataset. Often it i..."><img alt="" src="../../_images/sphx_glr_plot_feature_union_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/compose/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py"><span class="std std-ref">Concatenating multiple feature extraction methods</span></a></p>
<div class="sphx-glr-thumbnail-title">Concatenating multiple feature extraction methods</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A comparison for the decision boundaries generated on the iris dataset by Label Spreading, Self..."><img alt="" src="../../_images/sphx_glr_plot_semi_supervised_versus_svm_iris_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-versus-svm-iris-py"><span class="std std-ref">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only ..."><img alt="" src="../../_images/sphx_glr_plot_iris_svc_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py"><span class="std std-ref">Plot different SVM classifiers in the iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot different SVM classifiers in the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of the parameters gamma and C of the Radial Basis Function ..."><img alt="" src="../../_images/sphx_glr_plot_rbf_parameters_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py"><span class="std std-ref">RBF SVM parameters</span></a></p>
<div class="sphx-glr-thumbnail-title">RBF SVM parameters</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface..."><img alt="" src="../../_images/sphx_glr_plot_custom_kernel_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/svm/plot_custom_kernel.html#sphx-glr-auto-examples-svm-plot-custom-kernel-py"><span class="std std-ref">SVM with custom kernel</span></a></p>
<div class="sphx-glr-thumbnail-title">SVM with custom kernel</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform univariate feature selection before running a SVC (support ve..."><img alt="" src="../../_images/sphx_glr_plot_svm_anova_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/svm/plot_svm_anova.html#sphx-glr-auto-examples-svm-plot-svm-anova-py"><span class="std std-ref">SVM-Anova: SVM with univariate feature selection</span></a></p>
<div class="sphx-glr-thumbnail-title">SVM-Anova: SVM with univariate feature selection</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise for using different SVM kernels."><img alt="" src="../../_images/sphx_glr_plot_iris_exercise_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/exercises/plot_iris_exercise.html#sphx-glr-auto-examples-exercises-plot-iris-exercise-py"><span class="std std-ref">SVM Exercise</span></a></p>
<div class="sphx-glr-thumbnail-title">SVM Exercise</div>
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