module mlmodel.piecewise_tree_regression#
Short summary#
module mlinsights.mlmodel.piecewise_tree_regression
Implements a kind of piecewise linear regression by modifying the criterion used by the algorithm which builds a decision tree.
Classes#
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Implements a kind of piecewise linear regression by modifying the criterion used by the algorithm which builds a decision … |
Properties#
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HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should … |
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Return the feature importances. The importance of a feature is computed as the (normalized) total reduction … |
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DEPRECATED: The attribute n_features_ is deprecated in 1.0 and will be removed in 1.2. Use n_features_in_ instead. |
Methods#
method |
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Fits linear regressions for all leaves. Sets attributes |
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Computes the predictions with a linear regression fitted with the observations mapped to each leave of the … |
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Replaces the string stored in criterion by an instance of a class. |
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Overloads method predict. Falls back into the predict from a decision tree is criterion is mse, mae, … |
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Returns the leave index for each observation of X. |
Documentation#
Implements a kind of piecewise linear regression by modifying the criterion used by the algorithm which builds a decision tree.
- class mlinsights.mlmodel.piecewise_tree_regression.PiecewiseTreeRegressor(criterion='mselin', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0)#
Bases:
DecisionTreeRegressorImplements a kind of piecewise linear regression by modifying the criterion used by the algorithm which builds a decision tree. See sklearn.tree.DecisionTreeRegressor to get the meaning of the parameters except criterion:
mselin: optimizes for a piecewise linear regressionsimple: optimizes for a stepwise regression (equivalent to mse)
- __abstractmethods__ = frozenset({})#
- __init__(criterion='mselin', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0)#
- _abc_impl = <_abc._abc_data object>#
- _fit_reglin(X, y, sample_weight)#
Fits linear regressions for all leaves. Sets attributes
leaves_mapping_,betas_,leaves_index_. The first attribute is a dictionary{leave: row}which maps a leave of the tree to the coefficientsbetas_[row, :]of a regression trained on all training points mapped a specific leave.leaves_index_keeps in memory a set of leaves.
- _mapping_train(X)#
- _predict_reglin(X, check_input=True)#
Computes the predictions with a linear regression fitted with the observations mapped to each leave of the tree.
- Parameters:
X – array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, it will be converted to
dtype=np.float32and if a sparse matrix is provided to a sparsecsr_matrix.check_input – boolean, (default=True) Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
- Returns:
y, array of shape = [n_samples] or [n_samples, n_outputs] The predicted classes, or the predict values.
- fit(X, y, sample_weight=None, check_input=True)#
Replaces the string stored in criterion by an instance of a class.
- predict(X, check_input=True)#
Overloads method predict. Falls back into the predict from a decision tree is criterion is mse, mae, simple. Computes the predictions from linear regression if the criterion is mselin.
- predict_leaves(X)#
Returns the leave index for each observation of X.
- Parameters:
X – array
- Returns:
array leaves index in
self.leaves_index_