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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.
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 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).
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.
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 {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.
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.
