neural_trees.MultivariateDecisionTree#

class neural_trees.MultivariateDecisionTree(max_depth: int = 4, min_samples_split: int = 10, min_samples_leaf: int = 3, min_impurity_decrease: float = 0.0, random_state: int | None = None)[source]#

Bases: ClassifierMixin, BaseEstimator

Multivariate Decision Tree Classifier (sklearn-compatible).

Each internal node splits on a linear combination of all features, w . x + b > 0, where w is a linear discriminant fitted on the samples reaching that node. Boundaries are oblique rather than axis-aligned, so correlated features are handled in one node instead of a staircase of univariate cuts.

Parameters:

max_depthint, default=4: Maximum depth of the tree.
min_samples_splitint, default=10: Minimum samples required to attempt a split at a node.
min_samples_leafint, default=3: Minimum samples that must land on each side of a split.
min_impurity_decreasefloat, default=0.0: Minimum weighted Gini decrease required to keep a split.
random_stateint or None, default=None: Seed for the centroid clustering used to build two-group problems when a node holds more than two classes.

Attributes:

classes_ndarray of shape (n_classes,)
n_features_in_int
root_internal node object
tree_depth_int: Depth actually reached after fitting.
n_nodes_int: Number of internal (splitting) nodes.

References

Alpaydın, E., & Çetin, Ü. (1995). Multivariate Statistical Techniques for Constructive Induction. Yıldız, O. T., & Alpaydın, E. (2001). Omnivariate Decision Trees. IEEE TNN.

Examples

>>> from neural_trees import MultivariateDecisionTree
>>> from sklearn.datasets import load_wine
>>> X, y = load_wine(return_X_y=True)
>>> mdt = MultivariateDecisionTree(max_depth=3, random_state=0)
>>> mdt.fit(X, y)
>>> mdt.score(X, y)

fit(X, y) → MultivariateDecisionTree[source]#

Fit the multivariate tree.

Parameters:

Xarray-like of shape (n_samples, n_features)
yarray-like of shape (n_samples,)

Returns:

self

get_split_weights() → List[Tuple[ndarray, float]][source]#: Return the hyperplane of every internal node as (w, b) pairs, in pre-order. Useful for reading off which features drive a split.

predict(X) → ndarray[source]#

Predict class labels.

Parameters:

Xarray-like of shape (n_samples, n_features)

Returns:

y_predndarray of shape (n_samples,)

predict_proba(X) → ndarray[source]#

Predict class probabilities from the reached leaf’s class distribution.

Parameters:

Xarray-like of shape (n_samples, n_features)

Returns:

probandarray of shape (n_samples, n_classes)

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MultivariateDecisionTree#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.