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sklearn.neural_network._multilayer_perceptron.MLPClassifier

sklearn.neural_network._multilayer_perceptron.MLPClassifier

Visibility: public Uploaded 15-11-2022 by Laurens Krudde sklearn==1.1.3 numpy>=1.17.3 scipy>=1.3.2 joblib>=1.0.0 threadpoolctl>=2.0.0 1 runs
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  • openml-python python scikit-learn sklearn sklearn_1.1.3
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Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18

Parameters

activationdefault: "relu"
alphaStrength of the L2 regularization term. The L2 regularization term is divided by the sample size when added to the lossdefault: 1
batch_sizeSize of minibatches for stochastic optimizers If the solver is 'lbfgs', the classifier will not use minibatch When set to "auto", `batch_size=min(200, n_samples)` learning_rate : {'constant', 'invscaling', 'adaptive'}, default='constant' Learning rate schedule for weight updates - 'constant' is a constant learning rate given by 'learning_rate_init' - '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) - '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 Only used when ``solver='sgd'``default: "auto"
beta_1Exponential decay rate for estimates of first moment vector in adam, should be in [0, 1). Only used when solver='adam'default: 0.9
beta_2Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1). Only used when solver='adam'default: 0.999
early_stoppingWhether 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 tol for ``n_iter_no_change`` 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'default: false
epsilonValue for numerical stability in adam. Only used when solver='adam'default: 1e-08
hidden_layer_sizesThe ith element represents the number of neurons in the ith hidden layer activation : {'identity', 'logistic', 'tanh', 'relu'}, default='relu' Activation function for the hidden layer - 'identity', no-op activation, useful to implement linear bottleneck, returns f(x) = x - 'logistic', the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)) - 'tanh', the hyperbolic tan function, returns f(x) = tanh(x) - 'relu', the rectified linear unit function, returns f(x) = max(0, x) solver : {'lbfgs', 'sgd', 'adam'}, default='adam' The solver for weight optimization - 'lbfgs' is an optimizer in the family of quasi-Newton methods - 'sgd' refers to stochastic gradient descent - 'adam' refers to a stochastic gradient-based optimizer proposed by Kingma, Diederik, and Jimmy Ba Note: The default solver 'adam' works pretty well on relatively large datasets (with thousands of training samples or more) in terms of both training t...default: [100]
learning_ratedefault: "constant"
learning_rate_initThe initial learning rate used. It controls the step-size in updating the weights. Only used when solver='sgd' or 'adam'default: 0.001
max_funOnly 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 `MLPClassifier` .. versionadded:: 0.22default: 15000
max_iterMaximum 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 stepsdefault: 1000
momentumMomentum for gradient descent update. Should be between 0 and 1. Only used when solver='sgd'default: 0.9
n_iter_no_changeMaximum number of epochs to not meet ``tol`` improvement Only effective when solver='sgd' or 'adam' .. versionadded:: 0.20default: 10
nesterovs_momentumWhether to use Nesterov's momentum. Only used when solver='sgd' and momentum > 0default: true
power_tThe 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'default: 0.5
random_stateDetermines 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 :term:`Glossary `default: null
shuffleWhether to shuffle samples in each iteration. Only used when solver='sgd' or 'adam'default: true
solverdefault: "adam"
tolTolerance for the optimization. When the loss or score is not improving by at least ``tol`` for ``n_iter_no_change`` consecutive iterations, unless ``learning_rate`` is set to 'adaptive', convergence is considered to be reached and training stopsdefault: 0.0001
validation_fractionThe 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 Truedefault: 0.1
verboseWhether to print progress messages to stdoutdefault: false
warm_startWhen set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See :term:`the Glossary `default: false

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