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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.
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.
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.
