Module linear_model (0.23.0)

Linear models. This module is styled after scikit-learn's linear_model module:



    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.

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.


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

Logistic Regression (aka logit, MaxEnt) classifier.

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.