The linear regression surrogate discipline expresses the model output as a weighted sum of the model inputs:
where the coefficients \((w_1, w_2, ..., w_d)\) and the intercept \(w_0\) are estimated by least square regression. They are are easily accessible via the arguments coefficients and intercept.
The penalty level \(\alpha\) is a non-negative parameter intended to prevent overfitting, while the penalty ratio \(\lambda\in [0, 1]\) expresses the ratio between \(\ell_2\)- and \(\ell_1\)-regularization. When \(\lambda=1\), there is no \(\ell_1\)-regularization, and a Ridge regression is performed. When \(\lambda=0\), there is no \(\ell_2\)-regularization, and a Lasso regression is performed. For \(\lambda\) between 0 and 1, an elastic net regression is performed.
One may also choose not to penalize the regression at all, by setting \(\alpha=0\). In this case, a simple least squares regression is performed.
LinearRegression(data, transformer=None, input_names=None, output_names=None, fit_intercept=True, penalty_level=0.0, l2_penalty_ratio=1.0, **parameters)
data (Dataset) – learning dataset.
transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.
input_names (list(str)) – names of the input variables.
output_names (list(str)) – names of the output variables.
fit_intercept (bool) – if True, fit intercept. Default: True.
penalty_level (float) – penalty level greater or equal to 0. If 0, there is no penalty. Default: 0.
l2_penalty_ratio (float) – penalty ratio related to the l2 regularization. If 1, the penalty is the Ridge penalty. If 0, this is the Lasso penalty. Between 0 and 1, the penalty is the ElasticNet penalty. Default: None.