Basic configuration. You can skip configuration by providing no arguments.
In case no arguments are given you have to use the method fromConfig to set up a
configuration in order to use the method fit.
For the exact meaning of the arguments see the documentation of PolynomialFeatures.
Optionaldegree: numberHighest order of the monomials
Whether to include only highest order monomials
Whether to disallow higher powers of single features
Saves configuration to a simple option-bag
The configuration specifies the internal state of a PolynomialRegressor
completely. Hence the config of a fitted model can be used to save the model to a
file.
Number of input features.
Number of output features.
Instance of PolynomialFeatures responsible for transforming the input.
The weight matrix of the underlying linear regression model.
Loads configuration from simple option-bag.
The configuration to load from.
Model for performing multivariate polynomial regression.
Train the model by using
fit. Afterwards inference is possible viapredict.The model is implemented as a pipe consisting of two steps. First, the input is transformed by the class
PolynomialFeatures. This reduces the problem to a linear regression problem. Hence in the second step we simply apply linear regression.