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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"
] = "auto_strategy",
fit_intercept: bool = True,
l1_reg: typing.Optional[float] = None,
l2_reg: float = 0.0,
max_iterations: int = 20,
warm_start: bool = False,
learning_rate: typing.Optional[float] = None,
learning_rate_strategy: typing.Literal["line_search", "constant"] = "line_search",
tol: float = 0.01,
ls_init_learning_rate: typing.Optional[float] = None,
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 "auto_strategy"
The strategy to train linear regression models. Possible values are "auto_strategy", "batch_gradient_descent", "normal_equation". Default to "auto_strategy". |
fit_intercept |
bool, default True
Default |
l1_reg |
float or None, default None
The amount of L1 regularization applied. Default to None. Can't be set in "normal_equation" mode. If unset, value 0 is used. |
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. |
warm_start |
bool, default False
Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run. Default to False. |
learning_rate |
float or None, default None
The learn rate for gradient descent when learning_rate_strategy='constant'. If unset, value 0.1 is used. If learning_rate_strategy='line_search', an error is returned. |
learning_rate_strategy |
str, default "line_search"
The strategy for specifying the learning rate during training. Default to "line_search". |
tol |
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_learning_rate |
float or None, default None
Sets the initial learning rate that learning_rate_strategy='line_search' uses. This option can only be used if line_search is specified. If unset, value 0.1 is used. |
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(
*,
optimize_strategy: typing.Literal[
"auto_strategy", "batch_gradient_descent"
] = "auto_strategy",
fit_intercept: bool = True,
l1_reg: typing.Optional[float] = None,
l2_reg: float = 0.0,
max_iterations: int = 20,
warm_start: bool = False,
learning_rate: typing.Optional[float] = None,
learning_rate_strategy: typing.Literal["line_search", "constant"] = "line_search",
tol: float = 0.01,
ls_init_learning_rate: typing.Optional[float] = None,
calculate_p_values: bool = False,
enable_global_explain: bool = False,
class_weight: typing.Optional[
typing.Union[typing.Literal["balanced"], typing.Dict[str, float]]
] = None
)
Logistic Regression (aka logit, MaxEnt) classifier.
Parameters | |
---|---|
Name | Description |
optimize_strategy |
str, default "auto_strategy"
The strategy to train logistic regression models. Possible values are "auto_strategy" and "batch_gradient_descent". The two are equilevant since "auto_strategy" will fall back to "batch_gradient_descent". The API is kept for consistency. Default to "auto_strategy". |
fit_intercept |
default True
Default True. Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. |
class_weight |
dict or 'balanced', default None
Default None. Weights associated with classes in the form |
l1_reg |
float or None, default None
The amount of L1 regularization applied. Default to None. Can't be set in "normal_equation" mode. If unset, value 0 is used. |
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. |
warm_start |
bool, default False
Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run. Default to False. |
learning_rate |
float or None, default None
The learn rate for gradient descent when learning_rate_strategy='constant'. If unset, value 0.1 is used. If learning_rate_strategy='line_search', an error is returned. |
learning_rate_strategy |
str, default "line_search"
The strategy for specifying the learning rate during training. Default to "line_search". |
tol |
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_learning_rate |
float or None, default None
Sets the initial learning rate that learning_rate_strategy='line_search' uses. This option can only be used if line_search is specified. If unset, value 0.1 is used. |
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. |