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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 |
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 |