module grammar_sklearn.g_sklearn_linear_model

Short summary

module mlprodict.grammar_sklearn.g_sklearn_linear_model

List of interpreted from scikit-learn model.

source on GitHub

Functions

function

truncated documentation

sklearn_linear_regression

Converts a linear regression …

sklearn_logistic_regression

Interprets a logistic regression …

Documentation

List of interpreted from scikit-learn model.

source on GitHub

mlprodict.grammar_sklearn.g_sklearn_linear_model.sklearn_linear_regression(model, input_names=None, output_names=None, **kwargs)

Converts a linear regression into a grammar model (semantic graph representation).

Parameters

  • model – scikit-learn model

  • input_names – name of the input features

  • output_names – name of the output predictions

  • kwargs – additional parameter (with_loop)

Returns

graph model

If input is None or output is None, default values will be given to the outputs ['Prediction', 'Score'] for the outputs. If input_names is None, it wil be 'Features'.

Additional parameters: - with_loop: False by default, True not implemented. - dtype: float32 or float64

source on GitHub

mlprodict.grammar_sklearn.g_sklearn_linear_model.sklearn_logistic_regression(model, input_names=None, output_names=None, **kwargs)

Interprets a logistic regression model into a grammar model (semantic graph representation).

Parameters

  • model – scikit-learn model

  • input_names – name of the input features

  • output_names – name of the output predictions

  • kwargs – additional parameters (with_loop)

Returns

graph model

If input is None or output is None, default values will be given to the outputs ['Prediction', 'Score'] for the outputs. If input_names is None, it wil be 'Features'.

Additional parameters: - with_loop: False by default, True not implemented. - dtype: float32 or float64

source on GitHub

previous

module grammar_sklearn.g_sklearn_identify

next

module grammar_sklearn.g_sklearn_main