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  <section id="load-iris">
<h1>load_iris<a class="headerlink" href="#load-iris" title="Link 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/86d099ec1/sklearn/datasets/_base.py#L621"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_iris" title="Link 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>
<div class="pst-scrollable-table-container"><table class="table">
<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>
</div>
<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>
<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>
<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">Added 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">Added 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: ndarray of shape (3, )</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">Added 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">Added 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">Added in version 0.18.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<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">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_iris</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </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">&gt;&gt;&gt; </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">[np.str_(&#39;setosa&#39;), np.str_(&#39;versicolor&#39;), np.str_(&#39;virginica&#39;)]</span>
</pre></div>
</div>
<p>See <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">Principal Component Analysis (PCA) on Iris Dataset</span></a> for a more
detailed example of how to work with the iris dataset.</p>
</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="This example illustrates the use of sklearn.inspection.DecisionBoundaryDisplay to plot the predicted class probabilities of various classifiers in a 2D feature space, mostly for didactic purposes."><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="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="In many real-world examples, there are many ways to extract features from a dataset. Often it is beneficial to combine several methods to obtain good performance. This example shows how to use FeatureUnion to combine features obtained by PCA and univariate selection."><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="Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit in memory. IPCA builds a low-rank approximation for the input data using an amount of memory which is independent of the number of input data samples. It is still dependent on the input data features, but changing the batch size allows for control of memory usage."><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="This example shows a well known decomposition technique known as Principal Component Analysis (PCA) on 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">Principal Component Analysis (PCA) on Iris Dataset</span></a></p>
  <div class="sphx-glr-thumbnail-title">Principal Component Analysis (PCA) on Iris Dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width."><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 highly correlated. Sepal width is less redundant. Matrix decomposition techniques can uncover these latent patterns. Applying rotations to the resulting components does not inherently improve the predictive value of the derived latent space, but can help visualise their structure; here, for example, the varimax rotation, which is found by maximizing the squared variances of the weights, finds a structure where the second component only loads positively on sepal width."><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="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset."><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="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="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="This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the iris-dataset. The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by assigning different length-scales to the two feature dimensions."><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=" Train l1-penalized logistic regression models on a binary classification problem derived from the Iris dataset."><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="Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines."><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="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=&quot;pandas&quot;). 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="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="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 example compares non-nested and nested cross-validation strategies on a classifier of the iris data set. Nested cross-validation (CV) is often used to train a model in which hyperparameters also need to be optimized. Nested CV estimates the generalization error of the underlying model and its (hyper)parameter search. Choosing the parameters that maximize non-nested CV biases the model to the dataset, yielding an overly-optimistic score."><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="This example demonstrates the use of permutation_test_score to evaluate the significance of a cross-validated score using permutations."><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="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 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="This example presents how to estimate and visualize the variance of the Receiver Operating Characteristic (ROC) metric using cross-validation."><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 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="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 Nearest Centroid classification. It will plot the decision boundaries for each class."><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 visualizes some training loss curves for different stochastic learning strategies, including SGD and Adam. Because of time-constraints, we use several small datasets, for which L-BFGS might be more suitable. The general trend shown in these examples seems to carry over to larger datasets, however."><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="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 &lt;release_notes_0_22&gt;."><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="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 &lt;release_notes_0_24&gt;."><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 1.2! 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 &lt;release_notes_1_2&gt;."><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="A comparison for the decision boundaries generated on the iris dataset by Label Spreading, Self-training and SVM."><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="Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors."><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="Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset:"><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 (RBF) kernel SVM."><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="This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification scores. We use the iris dataset (4 features) and add 36 non-informative features. We can find that our model achieves best performance when we select around 10% of features."><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="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 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></section>
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