- 1.25.0 (latest)
- 1.24.0
- 1.22.0
- 1.21.0
- 1.20.0
- 1.19.0
- 1.18.0
- 1.17.0
- 1.16.0
- 1.15.0
- 1.14.0
- 1.13.0
- 1.12.0
- 1.11.1
- 1.10.0
- 1.9.0
- 1.8.0
- 1.7.0
- 1.6.0
- 1.5.0
- 1.4.0
- 1.3.0
- 1.2.0
- 1.1.0
- 1.0.0
- 0.26.0
- 0.25.0
- 0.24.0
- 0.23.0
- 0.22.0
- 0.21.0
- 0.20.1
- 0.19.2
- 0.18.0
- 0.17.0
- 0.16.0
- 0.15.0
- 0.14.1
- 0.13.0
- 0.12.0
- 0.11.0
- 0.10.0
- 0.9.0
- 0.8.0
- 0.7.0
- 0.6.0
- 0.5.0
- 0.4.0
- 0.3.0
- 0.2.0
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(fit_intercept=True)
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.
Parameter | |
---|---|
Name | Description |
fit_intercept |
default True
Default |
LogisticRegression
LogisticRegression(fit_intercept: bool = True, auto_class_weights: bool = False)
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. |
auto_class_weights |
default False
Default False. If True, balance class labels using weights for each class in inverse proportion to the frequency of that class. |