Options¶

This section documents the options components of the tree module.

options ¶

Defines sklearn.tree options interoperability.

This module provides functionality for interfacing with scikit-learn's tree-based algorithms within the Nextmv framework. It includes classes for configuring decision tree regressors.

CLASS	DESCRIPTION
`DecisionTreeRegressorOptions`	Options wrapper for scikit-learn's DecisionTreeRegressor.

DECISION_TREE_REGRESSOR_PARAMETERS `module-attribute` ¶

DECISION_TREE_REGRESSOR_PARAMETERS = [
    Option(
        name="criterion",
        option_type=str,
        choices=[
            "squared_error",
            "friedman_mse",
            "absolute_error",
            "poisson",
        ],
        description="The function to measure the quality of a split.",
        default="squared_error",
    ),
    Option(
        name="splitter",
        option_type=str,
        choices=["best", "random"],
        description="The strategy used to choose the split at each node.",
        default="best",
    ),
    Option(
        name="max_depth",
        option_type=int,
        description="The maximum depth of the tree.",
    ),
    Option(
        name="min_samples_split",
        option_type=int,
        description="The minimum number of samples required to split an internal node.",
    ),
    Option(
        name="min_samples_leaf",
        option_type=int,
        description="The minimum number of samples required to be at a leaf node.",
    ),
    Option(
        name="min_weight_fraction_leaf",
        option_type=float,
        description="The minimum weighted fraction of the sum total of weights required to be at a leaf node.",
    ),
    Option(
        name="max_features",
        option_type=int,
        description="The number of features to consider when looking for the best split.",
    ),
    Option(
        name="random_state",
        option_type=int,
        description="Controls the randomness of the estimator.",
    ),
    Option(
        name="max_leaf_nodes",
        option_type=int,
        description="Grow a tree with max_leaf_nodes in best-first fashion.",
    ),
    Option(
        name="min_impurity_decrease",
        option_type=float,
        description="A node will be split if this split induces a decrease of the impurity #.",
    ),
    Option(
        name="ccp_alpha",
        option_type=float,
        description="Complexity parameter used for Minimal Cost-Complexity Pruning.",
    ),
]

List of Nextmv Option objects for configuring a DecisionTreeRegressor.

Each option corresponds to a hyperparameter of the scikit-learn DecisionTreeRegressor, providing a consistent interface for setting up decision tree regression models within the Nextmv ecosystem.

You can import the DECISION_TREE_REGRESSOR_PARAMETERS directly from tree:

from nextmv_sklearn.tree import DECISION_TREE_REGRESSOR_PARAMETERS

DecisionTreeRegressorOptions ¶

DecisionTreeRegressorOptions()

Options for the sklearn.tree.DecisionTreeRegressor.

You can import the DecisionTreeRegressorOptions class directly from tree:

from nextmv_sklearn.tree import DecisionTreeRegressorOptions

A wrapper class for scikit-learn's DecisionTreeRegressor hyperparameters, providing a consistent interface for configuring decision tree regression models within the Nextmv ecosystem.

ATTRIBUTE	DESCRIPTION
`params`	List of Nextmv Option objects corresponding to DecisionTreeRegressor parameters. TYPE: `list`

Examples:

>>> from nextmv_sklearn.tree import DecisionTreeRegressorOptions
>>> options = DecisionTreeRegressorOptions()
>>> nextmv_options = options.to_nextmv()

Initialize a DecisionTreeRegressorOptions instance.

Configures the default parameters for a decision tree regressor.

Source code in nextmv-scikit-learn/nextmv_sklearn/tree/options.py

def __init__(self):
    """Initialize a DecisionTreeRegressorOptions instance.

    Configures the default parameters for a decision tree regressor.
    """
    self.params = DECISION_TREE_REGRESSOR_PARAMETERS

params `instance-attribute` ¶

params = DECISION_TREE_REGRESSOR_PARAMETERS

to_nextmv ¶

to_nextmv() -> Options

Converts the options to a Nextmv options object.

Creates a Nextmv Options instance from the configured decision tree regressor parameters.

RETURNS	DESCRIPTION
`Options`	A Nextmv options object containing all decision tree regressor parameters.