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<section id="oneclasssvm">
<h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">OneClassSVM</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">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</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">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/svm/_classes.py#L1595"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM" title="Link to this definition">#</a></dt>
<dd><p>Unsupervised Outlier Detection.</p>
<p>Estimate the support of a high-dimensional distribution.</p>
<p>The implementation is based on libsvm.</p>
<p>Read more in the <a class="reference internal" href="../outlier_detection.html#outlier-detection"><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>kernel</strong><span class="classifier">{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’</span></dt><dd><p>Specifies the kernel type to be used in the algorithm.
If none is given, ‘rbf’ will be used. If a callable is given it is
used to precompute the kernel matrix.</p>
</dd>
<dt><strong>degree</strong><span class="classifier">int, default=3</span></dt><dd><p>Degree of the polynomial kernel function (‘poly’).
Must be non-negative. Ignored by all other kernels.</p>
</dd>
<dt><strong>gamma</strong><span class="classifier">{‘scale’, ‘auto’} or float, default=’scale’</span></dt><dd><p>Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.</p>
<ul class="simple">
<li><p>if <code class="docutils literal notranslate"><span class="pre">gamma='scale'</span></code> (default) is passed then it uses
1 / (n_features * X.var()) as value of gamma,</p></li>
<li><p>if ‘auto’, uses 1 / n_features</p></li>
<li><p>if float, must be non-negative.</p></li>
</ul>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.22: </span>The default value of <code class="docutils literal notranslate"><span class="pre">gamma</span></code> changed from ‘auto’ to ‘scale’.</p>
</div>
</dd>
<dt><strong>coef0</strong><span class="classifier">float, default=0.0</span></dt><dd><p>Independent term in kernel function.
It is only significant in ‘poly’ and ‘sigmoid’.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-3</span></dt><dd><p>Tolerance for stopping criterion.</p>
</dd>
<dt><strong>nu</strong><span class="classifier">float, default=0.5</span></dt><dd><p>An upper bound on the fraction of training
errors and a lower bound of the fraction of support
vectors. Should be in the interval (0, 1]. By default 0.5
will be taken.</p>
</dd>
<dt><strong>shrinking</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to use the shrinking heuristic.
See the <a class="reference internal" href="../svm.html#shrinking-svm"><span class="std std-ref">User Guide</span></a>.</p>
</dd>
<dt><strong>cache_size</strong><span class="classifier">float, default=200</span></dt><dd><p>Specify the size of the kernel cache (in MB).</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">bool, default=False</span></dt><dd><p>Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in libsvm that, if enabled, may not work
properly in a multithreaded context.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=-1</span></dt><dd><p>Hard limit on iterations within solver, or -1 for no limit.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><a class="reference internal" href="#sklearn.svm.OneClassSVM.coef_" title="sklearn.svm.OneClassSVM.coef_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">coef_</span></code></a><span class="classifier">ndarray of shape (1, n_features)</span></dt><dd><p>Weights assigned to the features when <code class="docutils literal notranslate"><span class="pre">kernel="linear"</span></code>.</p>
</dd>
<dt><strong>dual_coef_</strong><span class="classifier">ndarray of shape (1, n_SV)</span></dt><dd><p>Coefficients of the support vectors in the decision function.</p>
</dd>
<dt><strong>fit_status_</strong><span class="classifier">int</span></dt><dd><p>0 if correctly fitted, 1 otherwise (will raise warning)</p>
</dd>
<dt><strong>intercept_</strong><span class="classifier">ndarray of shape (1,)</span></dt><dd><p>Constant in the decision function.</p>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">int</span></dt><dd><p>Number of iterations run by the optimization routine to fit the model.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.1.</span></p>
</div>
</dd>
<dt><a class="reference internal" href="#sklearn.svm.OneClassSVM.n_support_" title="sklearn.svm.OneClassSVM.n_support_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_support_</span></code></a><span class="classifier">ndarray of shape (n_classes,), dtype=int32</span></dt><dd><p>Number of support vectors for each class.</p>
</dd>
<dt><strong>offset_</strong><span class="classifier">float</span></dt><dd><p>Offset used to define the decision function from the raw scores.
We have the relation: decision_function = score_samples - <code class="docutils literal notranslate"><span class="pre">offset_</span></code>.
The offset is the opposite of <code class="docutils literal notranslate"><span class="pre">intercept_</span></code> and is provided for
consistency with other outlier detection algorithms.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.20.</span></p>
</div>
</dd>
<dt><strong>shape_fit_</strong><span class="classifier">tuple of int of shape (n_dimensions_of_X,)</span></dt><dd><p>Array dimensions of training vector <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
</dd>
<dt><strong>support_</strong><span class="classifier">ndarray of shape (n_SV,)</span></dt><dd><p>Indices of support vectors.</p>
</dd>
<dt><strong>support_vectors_</strong><span class="classifier">ndarray of shape (n_SV, n_features)</span></dt><dd><p>Support vectors.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.linear_model.SGDOneClassSVM.html#sklearn.linear_model.SGDOneClassSVM" title="sklearn.linear_model.SGDOneClassSVM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.linear_model.SGDOneClassSVM</span></code></a></dt><dd><p>Solves linear One-Class SVM using Stochastic Gradient Descent.</p>
</dd>
<dt><a class="reference internal" href="sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor" title="sklearn.neighbors.LocalOutlierFactor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.neighbors.LocalOutlierFactor</span></code></a></dt><dd><p>Unsupervised Outlier Detection using Local Outlier Factor (LOF).</p>
</dd>
<dt><a class="reference internal" href="sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest" title="sklearn.ensemble.IsolationForest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.ensemble.IsolationForest</span></code></a></dt><dd><p>Isolation Forest Algorithm.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</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.svm</span> <span class="kn">import</span> <span class="n">OneClassSVM</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.44</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.45</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.46</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">OneClassSVM</span><span class="p">(</span><span class="n">gamma</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([-1, 1, 1, 1, -1])</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">score_samples</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([1.7798..., 2.0547..., 2.0556..., 2.0561..., 1.7332...])</span>
</pre></div>
</div>
<p>For a more extended example,
see <a class="reference internal" href="../../auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">Species distribution modeling</span></a></p>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.coef_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">coef_</span></span><a class="headerlink" href="#sklearn.svm.OneClassSVM.coef_" title="Link to this definition">#</a></dt>
<dd><p>Weights assigned to the features when <code class="docutils literal notranslate"><span class="pre">kernel="linear"</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt>ndarray of shape (n_features, n_classes)</dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.decision_function">
<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/svm/_classes.py#L1798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.decision_function" title="Link to this definition">#</a></dt>
<dd><p>Signed distance to the separating hyperplane.</p>
<p>Signed distance is positive for an inlier and negative for an outlier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data matrix.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>dec</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Returns the decision function of the samples.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/svm/_classes.py#L1769"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.fit" title="Link to this definition">#</a></dt>
<dd><p>Detect the soft boundary of the set of samples X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Set of samples, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Per-sample weights. Rescale C per sample. Higher weights
force the classifier to put more emphasis on these points.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>If X is not a C-ordered contiguous array it is copied.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.fit_predict">
<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L987"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.fit_predict" title="Link to this definition">#</a></dt>
<dd><p>Perform fit on X and returns labels for X.</p>
<p>Returns -1 for outliers and 1 for inliers.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The input samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
<dt><strong>**kwargs</strong><span class="classifier">dict</span></dt><dd><p>Arguments to be passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>1 for inliers, -1 for outliers.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L1497"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.get_metadata_routing" title="Link to this definition">#</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.get_params" title="Link to this definition">#</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.n_support_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_support_</span></span><a class="headerlink" href="#sklearn.svm.OneClassSVM.n_support_" title="Link to this definition">#</a></dt>
<dd><p>Number of support vectors for each class.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/svm/_classes.py#L1831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.predict" title="Link to this definition">#</a></dt>
<dd><p>Perform classification on samples in X.</p>
<p>For a one-class model, +1 or -1 is returned.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train)</span></dt><dd><p>For kernel=”precomputed”, the expected shape of X is
(n_samples_test, n_samples_train).</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_pred</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Class labels for samples in X.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.score_samples">
<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/svm/_classes.py#L1816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.score_samples" title="Link to this definition">#</a></dt>
<dd><p>Raw scoring function of the samples.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data matrix.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score_samples</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Returns the (unshifted) scoring function of the samples.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.set_fit_request">
<span class="sig-name descname"><span class="pre">set_fit_request</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">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.OneClassSVM" title="sklearn.svm._classes.OneClassSVM"><span class="pre">OneClassSVM</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.set_fit_request" title="Link to this definition">#</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
Please see <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.set_params" title="Link to this definition">#</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</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 need for robust covariance estimation on a real data set. It is useful both for outlier detection and for a better understanding of the data structure."><img alt="" src="../../_images/sphx_glr_plot_outlier_detection_wine_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_outlier_detection_wine.html#sphx-glr-auto-examples-applications-plot-outlier-detection-wine-py"><span class="std std-ref">Outlier detection on a real data set</span></a></p>
<div class="sphx-glr-thumbnail-title">Outlier detection on a real data set</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Modeling species' geographic distributions is an important problem in conservation biology. In this example, we model the geographic distribution of two South American mammals given past observations and 14 environmental variables. Since we have only positive examples (there are no unsuccessful observations), we cast this problem as a density estimation problem and use the OneClassSVM as our modeling tool. The dataset is provided by Phillips et. al. (2006). If available, the example uses basemap to plot the coast lines and national boundaries of South America."><img alt="" src="../../_images/sphx_glr_plot_species_distribution_modeling_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">Species distribution modeling</span></a></p>
<div class="sphx-glr-thumbnail-title">Species distribution modeling</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to approximate the solution of sklearn.svm.OneClassSVM in the case of an RBF kernel with sklearn.linear_model.SGDOneClassSVM, a Stochastic Gradient Descent (SGD) version of the One-Class SVM. A kernel approximation is first used in order to apply sklearn.linear_model.SGDOneClassSVM which implements a linear One-Class SVM using SGD."><img alt="" src="../../_images/sphx_glr_plot_sgdocsvm_vs_ocsvm_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.html#sphx-glr-auto-examples-linear-model-plot-sgdocsvm-vs-ocsvm-py"><span class="std std-ref">One-Class SVM versus One-Class SVM using Stochastic Gradient Descent</span></a></p>
<div class="sphx-glr-thumbnail-title">One-Class SVM versus One-Class SVM using Stochastic Gradient Descent</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different anomaly detection algorithms on 2D datasets. Datasets contain one or two modes (regions of high density) to illustrate the ability of algorithms to cope with multimodal data."><img alt="" src="../../_images/sphx_glr_plot_anomaly_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_anomaly_comparison.html#sphx-glr-auto-examples-miscellaneous-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing anomaly detection algorithms for outlier detection on toy datasets</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example using a one-class SVM for novelty detection."><img alt="" src="../../_images/sphx_glr_plot_oneclass_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/svm/plot_oneclass.html#sphx-glr-auto-examples-svm-plot-oneclass-py"><span class="std std-ref">One-class SVM with non-linear kernel (RBF)</span></a></p>
<div class="sphx-glr-thumbnail-title">One-class SVM with non-linear kernel (RBF)</div>
</div></div></section>
</section>
</article>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.svm.OneClassSVM"><code class="docutils literal notranslate"><span class="pre">OneClassSVM</span></code></a><ul class="nav section-nav flex-column visible">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.svm.OneClassSVM.coef_"><code class="docutils literal notranslate"><span class="pre">coef_</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.svm.OneClassSVM.decision_function"><code class="docutils literal notranslate"><span class="pre">decision_function</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.svm.OneClassSVM.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.svm.OneClassSVM.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.svm.OneClassSVM.score_samples"><code class="docutils literal notranslate"><span class="pre">score_samples</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.svm.OneClassSVM.set_fit_request"><code class="docutils literal notranslate"><span class="pre">set_fit_request</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.svm.OneClassSVM.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
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