Module linear_model (0.20.1)

Version 0.20.1

• 1.26.0 (latest)
• 1.25.0
• 1.24.0
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0
• 1.15.0
• 1.14.0
• 1.13.0
• 1.12.0
• 1.11.1
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.1
• 0.19.2
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.1
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.0
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0

Linear models. This module is styled after scikit-learn's linear_model module: https://scikit-learn.org/stable/modules/linear_model.html.

Classes

LinearRegression

LinearRegression(
    optimize_strategy: typing.Literal[
        "auto_strategy", "batch_gradient_descent", "normal_equation"
    ] = "normal_equation",
    fit_intercept: bool = True,
    l2_reg: float = 0.0,
    max_iterations: int = 20,
    learn_rate_strategy: typing.Literal["line_search", "constant"] = "line_search",
    early_stop: bool = True,
    min_rel_progress: float = 0.01,
    ls_init_learn_rate: float = 0.1,
    calculate_p_values: bool = False,
    enable_global_explain: bool = False,
)

Ordinary least squares Linear Regression.

LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.

Parameters
Name	Description
optimize_strategy	str, default "normal_equation" The strategy to train linear regression models. Possible values are "auto_strategy", "batch_gradient_descent", "normal_equation". Default to "normal_equation".
fit_intercept	bool, default True Default True. Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).
l2_reg	float, default 0.0 The amount of L2 regularization applied. Default to 0.
max_iterations	int, default 20 The maximum number of training iterations or steps. Default to 20.
learn_rate_strategy	str, default "line_search" The strategy for specifying the learning rate during training. Default to "line_search".
early_stop	bool, default True Whether training should stop after the first iteration in which the relative loss improvement is less than the value specified for min_rel_progress. Default to True.
min_rel_progress	float, default 0.01 The minimum relative loss improvement that is necessary to continue training when EARLY_STOP is set to true. For example, a value of 0.01 specifies that each iteration must reduce the loss by 1% for training to continue. Default to 0.01.
ls_init_learn_rate	float, default 0.1 Sets the initial learning rate that learn_rate_strategy='line_search' uses. This option can only be used if line_search is specified. Default to 0.1.
calculate_p_values	bool, default False Specifies whether to compute p-values and standard errors during training. Default to False.
enable_global_explain	bool, default False Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False.

LogisticRegression

LogisticRegression(
    fit_intercept: bool = True,
    class_weights: typing.Optional[
        typing.Union[typing.Literal["balanced"], typing.Dict[str, float]]
    ] = None,
)

Logistic Regression (aka logit, MaxEnt) classifier.

Parameters
Name	Description
fit_intercept	default True Default True. Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.
class_weights	dict or 'balanced', default None Default None. Weights associated with classes in the form {class_label: weight}.If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)). Dict isn't supported now.