Module linear_model (0.13.0)

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.