X : array-like or sparse matrix, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array, shape = [n_samples]
Target vector relative to X
loss : {‘squared_hinge’, ‘log’}, default ‘squared_hinge’
Specifies the loss function. With ‘squared_hinge’ it is the squared hinge loss (a.k.a. L2 loss). With ‘log’ it is the loss of logistic regression models. ‘l2’ is accepted as an alias for ‘squared_hinge’, for backward compatibility reasons, but should not be used in new code.
fit_intercept : bool, default: True
Specifies if the intercept should be fitted by the model. It must match the fit() method parameter.
intercept_scaling : float, default: 1
when fit_intercept is True, instance vector x becomes [x, intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. It must match the fit() method parameter.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4