SoftmaxRegression

SoftmaxRegression(eta=0.01, epochs=50, l2=0.0, minibatches=1, n_classes=None, random_seed=None, print_progress=0)

Softmax regression classifier.

Parameters

Attributes

Examples

For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/classifier/SoftmaxRegression/

Methods


fit(X, y, init_params=True)

Learn model from training data.

Parameters

Returns


get_params(deep=True)

Get parameters for this estimator.

Parameters

Returns


predict(X)

Predict targets from X.

Parameters

Returns


predict_proba(X)

Predict class probabilities of X from the net input.

Parameters

Returns


score(X, y)

Compute the prediction accuracy

Parameters

Returns


set_params(params)

Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Returns

self

adapted from https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py

Author: Gael Varoquaux gael.varoquaux@normalesup.org

License: BSD 3 clause