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<section id="mlpclassifier">
<h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neural_network.</span></span><span class="sig-name descname"><span class="pre">MLPClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hidden_layer_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(100,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'relu'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate_init</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">power_t</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">max_iter</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">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</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">warm_start</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">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nesterovs_momentum</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">early_stopping</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">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/neural_network/_multilayer_perceptron.py#L874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier" title="Link to this definition">#</a></dt>
<dd><p>Multi-layer Perceptron classifier.</p>
<p>This model optimizes the log-loss function using LBFGS or stochastic
gradient descent.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.18.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>hidden_layer_sizes</strong><span class="classifier">array-like of shape(n_layers - 2,), default=(100,)</span></dt><dd><p>The ith element represents the number of neurons in the ith
hidden layer.</p>
</dd>
<dt><strong>activation</strong><span class="classifier">{‘identity’, ‘logistic’, ‘tanh’, ‘relu’}, default=’relu’</span></dt><dd><p>Activation function for the hidden layer.</p>
<ul class="simple">
<li><p>‘identity’, no-op activation, useful to implement linear bottleneck,
returns f(x) = x</p></li>
<li><p>‘logistic’, the logistic sigmoid function,
returns f(x) = 1 / (1 + exp(-x)).</p></li>
<li><p>‘tanh’, the hyperbolic tan function,
returns f(x) = tanh(x).</p></li>
<li><p>‘relu’, the rectified linear unit function,
returns f(x) = max(0, x)</p></li>
</ul>
</dd>
<dt><strong>solver</strong><span class="classifier">{‘lbfgs’, ‘sgd’, ‘adam’}, default=’adam’</span></dt><dd><p>The solver for weight optimization.</p>
<ul class="simple">
<li><p>‘lbfgs’ is an optimizer in the family of quasi-Newton methods.</p></li>
<li><p>‘sgd’ refers to stochastic gradient descent.</p></li>
<li><p>‘adam’ refers to a stochastic gradient-based optimizer proposed
by Kingma, Diederik, and Jimmy Ba</p></li>
</ul>
<p>For a comparison between Adam optimizer and SGD, see
<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>
<p>Note: The default solver ‘adam’ works pretty well on relatively
large datasets (with thousands of training samples or more) in terms of
both training time and validation score.
For small datasets, however, ‘lbfgs’ can converge faster and perform
better.</p>
</dd>
<dt><strong>alpha</strong><span class="classifier">float, default=0.0001</span></dt><dd><p>Strength of the L2 regularization term. The L2 regularization term
is divided by the sample size when added to the loss.</p>
<p>For an example usage and visualization of varying regularization, see
<a class="reference internal" href="../../auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py"><span class="std std-ref">Varying regularization in Multi-layer Perceptron</span></a>.</p>
</dd>
<dt><strong>batch_size</strong><span class="classifier">int, default=’auto’</span></dt><dd><p>Size of minibatches for stochastic optimizers.
If the solver is ‘lbfgs’, the classifier will not use minibatch.
When set to “auto”, <code class="docutils literal notranslate"><span class="pre">batch_size=min(200,</span> <span class="pre">n_samples)</span></code>.</p>
</dd>
<dt><strong>learning_rate</strong><span class="classifier">{‘constant’, ‘invscaling’, ‘adaptive’}, default=’constant’</span></dt><dd><p>Learning rate schedule for weight updates.</p>
<ul class="simple">
<li><p>‘constant’ is a constant learning rate given by
‘learning_rate_init’.</p></li>
<li><p>‘invscaling’ gradually decreases the learning rate at each
time step ‘t’ using an inverse scaling exponent of ‘power_t’.
effective_learning_rate = learning_rate_init / pow(t, power_t)</p></li>
<li><p>‘adaptive’ keeps the learning rate constant to
‘learning_rate_init’ as long as training loss keeps decreasing.
Each time two consecutive epochs fail to decrease training loss by at
least tol, or fail to increase validation score by at least tol if
‘early_stopping’ is on, the current learning rate is divided by 5.</p></li>
</ul>
<p>Only used when <code class="docutils literal notranslate"><span class="pre">solver='sgd'</span></code>.</p>
</dd>
<dt><strong>learning_rate_init</strong><span class="classifier">float, default=0.001</span></dt><dd><p>The initial learning rate used. It controls the step-size
in updating the weights. Only used when solver=’sgd’ or ‘adam’.</p>
</dd>
<dt><strong>power_t</strong><span class="classifier">float, default=0.5</span></dt><dd><p>The exponent for inverse scaling learning rate.
It is used in updating effective learning rate when the learning_rate
is set to ‘invscaling’. Only used when solver=’sgd’.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=200</span></dt><dd><p>Maximum number of iterations. The solver iterates until convergence
(determined by ‘tol’) or this number of iterations. For stochastic
solvers (‘sgd’, ‘adam’), note that this determines the number of epochs
(how many times each data point will be used), not the number of
gradient steps.</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to shuffle samples in each iteration. Only used when
solver=’sgd’ or ‘adam’.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance, default=None</span></dt><dd><p>Determines random number generation for weights and bias
initialization, train-test split if early stopping is used, and batch
sampling when solver=’sgd’ or ‘adam’.
Pass an int for reproducible results across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-4</span></dt><dd><p>Tolerance for the optimization. When the loss or score is not improving
by at least <code class="docutils literal notranslate"><span class="pre">tol</span></code> for <code class="docutils literal notranslate"><span class="pre">n_iter_no_change</span></code> consecutive iterations,
unless <code class="docutils literal notranslate"><span class="pre">learning_rate</span></code> is set to ‘adaptive’, convergence is
considered to be reached and training stops.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to print progress messages to stdout.</p>
</dd>
<dt><strong>warm_start</strong><span class="classifier">bool, default=False</span></dt><dd><p>When set to True, reuse the solution of the previous
call to fit as initialization, otherwise, just erase the
previous solution. See <a class="reference internal" href="../../glossary.html#term-warm_start"><span class="xref std std-term">the Glossary</span></a>.</p>
</dd>
<dt><strong>momentum</strong><span class="classifier">float, default=0.9</span></dt><dd><p>Momentum for gradient descent update. Should be between 0 and 1. Only
used when solver=’sgd’.</p>
</dd>
<dt><strong>nesterovs_momentum</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to use Nesterov’s momentum. Only used when solver=’sgd’ and
momentum > 0.</p>
</dd>
<dt><strong>early_stopping</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to use early stopping to terminate training when validation
score is not improving. If set to true, it will automatically set
aside 10% of training data as validation and terminate training when
validation score is not improving by at least <code class="docutils literal notranslate"><span class="pre">tol</span></code> for
<code class="docutils literal notranslate"><span class="pre">n_iter_no_change</span></code> consecutive epochs. The split is stratified,
except in a multilabel setting.
If early stopping is False, then the training stops when the training
loss does not improve by more than tol for n_iter_no_change consecutive
passes over the training set.
Only effective when solver=’sgd’ or ‘adam’.</p>
</dd>
<dt><strong>validation_fraction</strong><span class="classifier">float, default=0.1</span></dt><dd><p>The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
Only used if early_stopping is True.</p>
</dd>
<dt><strong>beta_1</strong><span class="classifier">float, default=0.9</span></dt><dd><p>Exponential decay rate for estimates of first moment vector in adam,
should be in [0, 1). Only used when solver=’adam’.</p>
</dd>
<dt><strong>beta_2</strong><span class="classifier">float, default=0.999</span></dt><dd><p>Exponential decay rate for estimates of second moment vector in adam,
should be in [0, 1). Only used when solver=’adam’.</p>
</dd>
<dt><strong>epsilon</strong><span class="classifier">float, default=1e-8</span></dt><dd><p>Value for numerical stability in adam. Only used when solver=’adam’.</p>
</dd>
<dt><strong>n_iter_no_change</strong><span class="classifier">int, default=10</span></dt><dd><p>Maximum number of epochs to not meet <code class="docutils literal notranslate"><span class="pre">tol</span></code> improvement.
Only effective when solver=’sgd’ or ‘adam’.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.20.</span></p>
</div>
</dd>
<dt><strong>max_fun</strong><span class="classifier">int, default=15000</span></dt><dd><p>Only used when solver=’lbfgs’. Maximum number of loss function calls.
The solver iterates until convergence (determined by ‘tol’), number
of iterations reaches max_iter, or this number of loss function calls.
Note that number of loss function calls will be greater than or equal
to the number of iterations for the <code class="docutils literal notranslate"><span class="pre">MLPClassifier</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.22.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>classes_</strong><span class="classifier">ndarray or list of ndarray of shape (n_classes,)</span></dt><dd><p>Class labels for each output.</p>
</dd>
<dt><strong>loss_</strong><span class="classifier">float</span></dt><dd><p>The current loss computed with the loss function.</p>
</dd>
<dt><strong>best_loss_</strong><span class="classifier">float or None</span></dt><dd><p>The minimum loss reached by the solver throughout fitting.
If <code class="docutils literal notranslate"><span class="pre">early_stopping=True</span></code>, this attribute is set to <code class="docutils literal notranslate"><span class="pre">None</span></code>. Refer to
the <code class="docutils literal notranslate"><span class="pre">best_validation_score_</span></code> fitted attribute instead.</p>
</dd>
<dt><strong>loss_curve_</strong><span class="classifier">list of shape (<code class="docutils literal notranslate"><span class="pre">n_iter_</span></code>,)</span></dt><dd><p>The ith element in the list represents the loss at the ith iteration.</p>
</dd>
<dt><strong>validation_scores_</strong><span class="classifier">list of shape (<code class="docutils literal notranslate"><span class="pre">n_iter_</span></code>,) or None</span></dt><dd><p>The score at each iteration on a held-out validation set. The score
reported is the accuracy score. Only available if <code class="docutils literal notranslate"><span class="pre">early_stopping=True</span></code>,
otherwise the attribute is set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
</dd>
<dt><strong>best_validation_score_</strong><span class="classifier">float or None</span></dt><dd><p>The best validation score (i.e. accuracy score) that triggered the
early stopping. Only available if <code class="docutils literal notranslate"><span class="pre">early_stopping=True</span></code>, otherwise the
attribute is set to <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
</dd>
<dt><strong>t_</strong><span class="classifier">int</span></dt><dd><p>The number of training samples seen by the solver during fitting.</p>
</dd>
<dt><strong>coefs_</strong><span class="classifier">list of shape (n_layers - 1,)</span></dt><dd><p>The ith element in the list represents the weight matrix corresponding
to layer i.</p>
</dd>
<dt><strong>intercepts_</strong><span class="classifier">list of shape (n_layers - 1,)</span></dt><dd><p>The ith element in the list represents the bias vector corresponding to
layer i + 1.</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>The number of iterations the solver has run.</p>
</dd>
<dt><strong>n_layers_</strong><span class="classifier">int</span></dt><dd><p>Number of layers.</p>
</dd>
<dt><strong>n_outputs_</strong><span class="classifier">int</span></dt><dd><p>Number of outputs.</p>
</dd>
<dt><strong>out_activation_</strong><span class="classifier">str</span></dt><dd><p>Name of the output activation function.</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.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network.MLPRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MLPRegressor</span></code></a></dt><dd><p>Multi-layer Perceptron regressor.</p>
</dd>
<dt><a class="reference internal" href="sklearn.neural_network.BernoulliRBM.html#sklearn.neural_network.BernoulliRBM" title="sklearn.neural_network.BernoulliRBM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BernoulliRBM</span></code></a></dt><dd><p>Bernoulli Restricted Boltzmann Machine (RBM).</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>MLPClassifier trains iteratively since at each time step
the partial derivatives of the loss function with respect to the model
parameters are computed to update the parameters.</p>
<p>It can also have a regularization term added to the loss function
that shrinks model parameters to prevent overfitting.</p>
<p>This implementation works with data represented as dense numpy arrays or
sparse scipy arrays of floating point values.</p>
<p class="rubric">References</p>
<p>Hinton, Geoffrey E. “Connectionist learning procedures.”
Artificial intelligence 40.1 (1989): 185-234.</p>
<p>Glorot, Xavier, and Yoshua Bengio.
“Understanding the difficulty of training deep feedforward neural networks.”
International Conference on Artificial Intelligence and Statistics. 2010.</p>
<p><a class="reference external" href="https://arxiv.org/abs/1502.01852">He, Kaiming, et al (2015). “Delving deep into rectifiers:
Surpassing human-level performance on imagenet classification.”</a></p>
<p><a class="reference external" href="https://arxiv.org/abs/1412.6980">Kingma, Diederik, and Jimmy Ba (2014)
“Adam: A method for stochastic optimization.”</a></p>
<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="w"> </span><span class="nn">sklearn.neural_network</span><span class="w"> </span><span class="kn">import</span> <span class="n">MLPClassifier</span>
<span class="gp">>>> </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">make_classification</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">100</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="gp">>>> </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">random_state</span><span class="o">=</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">MLPClassifier</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="n">max_iter</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
<span class="go">array([[0.038..., 0.961...]])</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_test</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:])</span>
<span class="go">array([1, 0, 1, 0, 1])</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="go">0.8...</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.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></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/86d099ec1/sklearn/neural_network/_multilayer_perceptron.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit the model to data matrix X and target(s) y.</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">ndarray or sparse matrix of shape (n_samples, n_features)</span></dt><dd><p>The input data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>The target values (class labels in classification, real numbers in
regression).</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.7.</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>self</strong><span class="classifier">object</span></dt><dd><p>Returns a trained MLP model.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.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/86d099ec1/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.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.neural_network.MLPClassifier.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/86d099ec1/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.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 method">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.partial_fit">
<span class="sig-name descname"><span class="pre">partial_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></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>, <em class="sig-param"><span class="n"><span class="pre">classes</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/86d099ec1/sklearn/neural_network/_multilayer_perceptron.py#L1292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.partial_fit" title="Link to this definition">#</a></dt>
<dd><p>Update the model with a single iteration over the given data.</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 data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>The target values.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.7.</span></p>
</div>
</dd>
<dt><strong>classes</strong><span class="classifier">array of shape (n_classes,), default=None</span></dt><dd><p>Classes across all calls to partial_fit.
Can be obtained via <code class="docutils literal notranslate"><span class="pre">np.unique(y_all)</span></code>, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn’t need to contain all labels in <code class="docutils literal notranslate"><span class="pre">classes</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>Trained MLP model.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.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/86d099ec1/sklearn/neural_network/_multilayer_perceptron.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict using the multi-layer perceptron classifier.</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>The input data.</p>
</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, shape (n_samples,) or (n_samples, n_classes)</span></dt><dd><p>The predicted classes.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.predict_log_proba">
<span class="sig-name descname"><span class="pre">predict_log_proba</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/86d099ec1/sklearn/neural_network/_multilayer_perceptron.py#L1332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
<dd><p>Return the log of probability estimates.</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">ndarray of shape (n_samples, n_features)</span></dt><dd><p>The input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>log_y_prob</strong><span class="classifier">ndarray of shape (n_samples, n_classes)</span></dt><dd><p>The predicted log-probability of the sample for each class
in the model, where classes are ordered as they are in
<code class="docutils literal notranslate"><span class="pre">self.classes_</span></code>. Equivalent to <code class="docutils literal notranslate"><span class="pre">log(predict_proba(X))</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</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/86d099ec1/sklearn/neural_network/_multilayer_perceptron.py#L1350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.predict_proba" title="Link to this definition">#</a></dt>
<dd><p>Probability estimates.</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>The input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_prob</strong><span class="classifier">ndarray of shape (n_samples, n_classes)</span></dt><dd><p>The predicted probability of the sample for each class in the
model, where classes are ordered as they are in <code class="docutils literal notranslate"><span class="pre">self.classes_</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.score">
<span class="sig-name descname"><span class="pre">score</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></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/86d099ec1/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.score" title="Link to this definition">#</a></dt>
<dd><p>Return <a class="reference internal" href="../model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
<p>In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.</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>Test samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True labels for <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Mean accuracy of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> w.r.t. <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.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.neural_network.MLPClassifier" title="sklearn.neural_network._multilayer_perceptron.MLPClassifier"><span class="pre">MLPClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.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.neural_network.MLPClassifier.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/86d099ec1/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.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>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.set_partial_fit_request">
<span class="sig-name descname"><span class="pre">set_partial_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">classes</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>, <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.neural_network.MLPClassifier" title="sklearn.neural_network._multilayer_perceptron.MLPClassifier"><span class="pre">MLPClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_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">partial_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">partial_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>classes</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">classes</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
</dd>
<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">partial_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.neural_network.MLPClassifier.set_score_request">
<span class="sig-name descname"><span class="pre">set_score_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.neural_network.MLPClassifier" title="sklearn.neural_network._multilayer_perceptron.MLPClassifier"><span class="pre">MLPClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_score_request" title="Link to this definition">#</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</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">score</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">score</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">score</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>
</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="A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets."><img alt="" src="../../_images/sphx_glr_plot_classifier_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</span></a></p>
<div class="sphx-glr-thumbnail-title">Classifier comparison</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A comparison of different values for regularization parameter 'alpha' on synthetic datasets. The plot shows that different alphas yield different decision functions."><img alt="" src="../../_images/sphx_glr_plot_mlp_alpha_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py"><span class="std std-ref">Varying regularization in Multi-layer Perceptron</span></a></p>
<div class="sphx-glr-thumbnail-title">Varying regularization in Multi-layer Perceptron</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="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="Sometimes looking at the learned coefficients of a neural network can provide insight into the learning behavior. For example if weights look unstructured, maybe some were not used at all, or if very large coefficients exist, maybe regularization was too low or the learning rate too high."><img alt="" src="../../_images/sphx_glr_plot_mnist_filters_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py"><span class="std std-ref">Visualization of MLP weights on MNIST</span></a></p>
<div class="sphx-glr-thumbnail-title">Visualization of MLP weights on MNIST</div>
</div></div></section>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier"><code class="docutils literal notranslate"><span class="pre">MLPClassifier</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.neural_network.MLPClassifier.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.partial_fit"><code class="docutils literal notranslate"><span class="pre">partial_fit</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.predict_log_proba"><code class="docutils literal notranslate"><span class="pre">predict_log_proba</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">predict_proba</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.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.neural_network.MLPClassifier.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.set_partial_fit_request"><code class="docutils literal notranslate"><span class="pre">set_partial_fit_request</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neural_network.MLPClassifier.set_score_request"><code class="docutils literal notranslate"><span class="pre">set_score_request</span></code></a></li>
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