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<li class="toctree-l1 has-children"><a class="reference internal" href="../supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/linear_model.html">1.1. Linear Models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/mixture.html">2.1. Gaussian mixture models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/clustering.html">2.3. Clustering</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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</details></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="../datasets.html">7. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/array_api.html">11.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">13. External Resources, Videos and Talks</a></li>
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<section id="generated-datasets">
<span id="sample-generators"></span><h1><span class="section-number">7.3. </span>Generated datasets<a class="headerlink" href="#generated-datasets" title="Link to this heading">#</a></h1>
<p>In addition, scikit-learn includes various random sample generators that
can be used to build artificial datasets of controlled size and complexity.</p>
<section id="generators-for-classification-and-clustering">
<h2><span class="section-number">7.3.1. </span>Generators for classification and clustering<a class="headerlink" href="#generators-for-classification-and-clustering" title="Link to this heading">#</a></h2>
<p>These generators produce a matrix of features and corresponding discrete
targets.</p>
<section id="single-label">
<h3><span class="section-number">7.3.1.1. </span>Single label<a class="headerlink" href="#single-label" title="Link to this heading">#</a></h3>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_blobs</span></code></a> creates a multiclass dataset by allocating each class to one
normally-distributed cluster of points. It provides control over the centers and
standard deviations of each cluster. This dataset is used to demonstrate clustering.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">centers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">cluster_std</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Three normally-distributed clusters"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<figure class="align-center">
<a class="plot-directive reference internal image-reference" href="../_images/sample_generators-1.png"><img alt="../_images/sample_generators-1.png" class="plot-directive" src="../_images/sample_generators-1.png" style="width: 358.4px; height: 268.79999999999995px;" />
</a>
</figure>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_classification</span></code></a> also creates multiclass datasets but specializes in
introducing noise by way of: correlated, redundant and uninformative features; multiple
Gaussian clusters per class; and linear transformations of the feature space.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">titles</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Two classes,</span><span class="se">\n</span><span class="s2">one informative feature,</span><span class="se">\n</span><span class="s2">one cluster per class"</span><span class="p">,</span>
<span class="s2">"Two classes,</span><span class="se">\n</span><span class="s2">two informative features,</span><span class="se">\n</span><span class="s2">two clusters per class"</span><span class="p">,</span>
<span class="s2">"Three classes,</span><span class="se">\n</span><span class="s2">two informative features,</span><span class="se">\n</span><span class="s2">one cluster per class"</span><span class="p">]</span>
<span class="n">params</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">{</span><span class="s2">"n_informative"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"n_clusters_per_class"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"n_classes"</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span>
<span class="p">{</span><span class="s2">"n_informative"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"n_clusters_per_class"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"n_classes"</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span>
<span class="p">{</span><span class="s2">"n_informative"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"n_clusters_per_class"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"n_classes"</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
<span class="p">]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">param</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">Y</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">titles</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<figure class="align-center">
<a class="plot-directive reference internal image-reference" href="../_images/sample_generators-2.png"><img alt="../_images/sample_generators-2.png" class="plot-directive" src="../_images/sample_generators-2.png" style="width: 672.0px; height: 224.0px;" />
</a>
</figure>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_gaussian_quantiles.html#sklearn.datasets.make_gaussian_quantiles" title="sklearn.datasets.make_gaussian_quantiles"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_gaussian_quantiles</span></code></a> divides a single Gaussian cluster into
near-equal-size classes separated by concentric hyperspheres.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_gaussian_quantiles</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">make_gaussian_quantiles</span><span class="p">(</span><span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">Y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Gaussian divided into three quantiles"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<figure class="align-center">
<a class="plot-directive reference internal image-reference" href="../_images/sample_generators-3.png"><img alt="../_images/sample_generators-3.png" class="plot-directive" src="../_images/sample_generators-3.png" style="width: 358.4px; height: 268.79999999999995px;" />
</a>
</figure>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_hastie_10_2.html#sklearn.datasets.make_hastie_10_2" title="sklearn.datasets.make_hastie_10_2"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_hastie_10_2</span></code></a> generates a similar binary, 10-dimensional problem.</p>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_circles</span></code></a> and <a class="reference internal" href="../modules/generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons" title="sklearn.datasets.make_moons"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_moons</span></code></a> generate 2D binary classification
datasets that are challenging to certain algorithms (e.g., centroid-based
clustering or linear classification), including optional Gaussian noise.
They are useful for visualization. <a class="reference internal" href="../modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_circles</span></code></a> produces Gaussian data
with a spherical decision boundary for binary classification, while
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons" title="sklearn.datasets.make_moons"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_moons</span></code></a> produces two interleaving half-circles.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_circles</span><span class="p">,</span> <span class="n">make_moons</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">make_circles</span><span class="p">(</span><span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">Y</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"make_circles"</span><span class="p">)</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">make_moons</span><span class="p">(</span><span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">Y</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"make_moons"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<figure class="align-center">
<a class="plot-directive reference internal image-reference" href="../_images/sample_generators-4.png"><img alt="../_images/sample_generators-4.png" class="plot-directive" src="../_images/sample_generators-4.png" style="width: 448.0px; height: 224.0px;" />
</a>
</figure>
</section>
<section id="multilabel">
<h3><span class="section-number">7.3.1.2. </span>Multilabel<a class="headerlink" href="#multilabel" title="Link to this heading">#</a></h3>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_multilabel_classification.html#sklearn.datasets.make_multilabel_classification" title="sklearn.datasets.make_multilabel_classification"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_multilabel_classification</span></code></a> generates random samples with multiple
labels, reflecting a bag of words drawn from a mixture of topics. The number of
topics for each document is drawn from a Poisson distribution, and the topics
themselves are drawn from a fixed random distribution. Similarly, the number of
words is drawn from Poisson, with words drawn from a multinomial, where each
topic defines a probability distribution over words. Simplifications with
respect to true bag-of-words mixtures include:</p>
<ul class="simple">
<li><p>Per-topic word distributions are independently drawn, where in reality all
would be affected by a sparse base distribution, and would be correlated.</p></li>
<li><p>For a document generated from multiple topics, all topics are weighted
equally in generating its bag of words.</p></li>
<li><p>Documents without labels words at random, rather than from a base
distribution.</p></li>
</ul>
<a class="reference external image-reference" href="../auto_examples/datasets/plot_random_multilabel_dataset.html"><img alt="../_images/sphx_glr_plot_random_multilabel_dataset_001.png" class="align-center" src="../_images/sphx_glr_plot_random_multilabel_dataset_001.png" style="width: 400.0px; height: 200.0px;" />
</a>
</section>
<section id="biclustering">
<h3><span class="section-number">7.3.1.3. </span>Biclustering<a class="headerlink" href="#biclustering" title="Link to this heading">#</a></h3>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_biclusters.html#sklearn.datasets.make_biclusters" title="sklearn.datasets.make_biclusters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_biclusters</span></code></a>(shape, n_clusters, *[, ...])</p></td>
<td><p>Generate a constant block diagonal structure array for biclustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_checkerboard.html#sklearn.datasets.make_checkerboard" title="sklearn.datasets.make_checkerboard"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_checkerboard</span></code></a>(shape, n_clusters, *[, ...])</p></td>
<td><p>Generate an array with block checkerboard structure for biclustering.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
</section>
<section id="generators-for-regression">
<h2><span class="section-number">7.3.2. </span>Generators for regression<a class="headerlink" href="#generators-for-regression" title="Link to this heading">#</a></h2>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_regression</span></code></a> produces regression targets as an optionally-sparse
random linear combination of random features, with noise. Its informative
features may be uncorrelated, or low rank (few features account for most of the
variance).</p>
<p>Other regression generators generate functions deterministically from
randomized features. <a class="reference internal" href="../modules/generated/sklearn.datasets.make_sparse_uncorrelated.html#sklearn.datasets.make_sparse_uncorrelated" title="sklearn.datasets.make_sparse_uncorrelated"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_sparse_uncorrelated</span></code></a> produces a target as a
linear combination of four features with fixed coefficients.
Others encode explicitly non-linear relations:
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_friedman1.html#sklearn.datasets.make_friedman1" title="sklearn.datasets.make_friedman1"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_friedman1</span></code></a> is related by polynomial and sine transforms;
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_friedman2.html#sklearn.datasets.make_friedman2" title="sklearn.datasets.make_friedman2"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_friedman2</span></code></a> includes feature multiplication and reciprocation; and
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_friedman3.html#sklearn.datasets.make_friedman3" title="sklearn.datasets.make_friedman3"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_friedman3</span></code></a> is similar with an arctan transformation on the target.</p>
</section>
<section id="generators-for-manifold-learning">
<h2><span class="section-number">7.3.3. </span>Generators for manifold learning<a class="headerlink" href="#generators-for-manifold-learning" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_s_curve.html#sklearn.datasets.make_s_curve" title="sklearn.datasets.make_s_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_s_curve</span></code></a>([n_samples, noise, random_state])</p></td>
<td><p>Generate an S curve dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_swiss_roll.html#sklearn.datasets.make_swiss_roll" title="sklearn.datasets.make_swiss_roll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_swiss_roll</span></code></a>([n_samples, noise, ...])</p></td>
<td><p>Generate a swiss roll dataset.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="generators-for-decomposition">
<h2><span class="section-number">7.3.4. </span>Generators for decomposition<a class="headerlink" href="#generators-for-decomposition" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_low_rank_matrix.html#sklearn.datasets.make_low_rank_matrix" title="sklearn.datasets.make_low_rank_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_low_rank_matrix</span></code></a>([n_samples, ...])</p></td>
<td><p>Generate a mostly low rank matrix with bell-shaped singular values.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_sparse_coded_signal.html#sklearn.datasets.make_sparse_coded_signal" title="sklearn.datasets.make_sparse_coded_signal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_sparse_coded_signal</span></code></a>(n_samples, *, ...)</p></td>
<td><p>Generate a signal as a sparse combination of dictionary elements.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_spd_matrix.html#sklearn.datasets.make_spd_matrix" title="sklearn.datasets.make_spd_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_spd_matrix</span></code></a>(n_dim, *[, random_state])</p></td>
<td><p>Generate a random symmetric, positive-definite matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_sparse_spd_matrix.html#sklearn.datasets.make_sparse_spd_matrix" title="sklearn.datasets.make_sparse_spd_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_sparse_spd_matrix</span></code></a>([n_dim, alpha, ...])</p></td>
<td><p>Generate a sparse symmetric definite positive matrix.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
</section>
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