module sklapi.sklearn_base_learner

Inheritance diagram of mlinsights.sklapi.sklearn_base_learner

Short summary

module mlinsights.sklapi.sklearn_base_learner

Implements a learner which follows the same API as every scikit-learn learner.

source on GitHub

Classes

class

truncated documentation

SkBaseLearner

Pattern of a learner qui suit la même API que scikit-learn.

Methods

method

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__init__

constructor

decision_function

Output of the model in case of a regressor, matrix with a score for each class and each sample for a classifier. …

fit

Trains a model.

predict

Predicts.

score

Returns the mean accuracy on the given test data and labels.

Documentation

Implements a learner which follows the same API as every scikit-learn learner.

source on GitHub

class mlinsights.sklapi.sklearn_base_learner.SkBaseLearner(**kwargs)[source]

Bases: mlinsights.sklapi.sklearn_base.SkBase

Pattern of a learner qui suit la même API que scikit-learn.

source on GitHub

constructor

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__init__(**kwargs)[source]

constructor

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decision_function(X)[source]

Output of the model in case of a regressor, matrix with a score for each class and each sample for a classifier.

Parameters

X – Samples, {array-like, sparse matrix}, shape = (n_samples, n_features)

Returns

array, shape = (n_samples,.), Returns predicted values.

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fit(X, y=None, sample_weight=None)[source]

Trains a model.

Parameters
  • X – features

  • y – targets

  • sample_weight – weight

Returns

self

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predict(X)[source]

Predicts.

Parameters

X – features

Returns

prédictions

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score(X, y=None, sample_weight=None)[source]

Returns the mean accuracy on the given test data and labels.

Parameters
  • X – Training data, numpy array or sparse matrix of shape [n_samples,n_features]

  • y – Target values, numpy array of shape [n_samples, n_targets] (optional)

  • sample_weight – Weight values, numpy array of shape [n_samples, n_targets] (optional)

Returns

score : float, Mean accuracy of self.predict(X) wrt. y.

source on GitHub