LinearRegressionModel
Related Docs:
object LinearRegressionModel
| package ml
Vector of coefficients of the model. Must match the length of inputFeatures.
Description of the (numeric) input features. Must match the length of coefficients.
Intercept of the model (defaults to 0).
Name used to identify the dependent feature in an evaluation dataset.
Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.
This should return a representative class from every jar that is needed to load the object during deserialization.
This should return a representative class from every jar that is needed to load the object during deserialization.
Default implementation returns the class of MLModel, and the class of the model implementation.
Vector of coefficients of the model.
Vector of coefficients of the model. Must match the length of inputFeatures.
Used when we are doing model quality evaluation e.g.
Used when we are doing model quality evaluation e.g. Confusion Matrix, so we know what feature in the test dataset to compare the result to.
Feature description used to identify the dependent feature in an evaluation dataset.
Name used to identify the dependent feature in an evaluation dataset.
Used to identify this model when in a collection of models.
Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.
Description of the (numeric) input features.
Description of the (numeric) input features. Must match the length of coefficients.
Intercept of the model (defaults to 0).
Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.
Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.
Behaviour is undefined if requiredOutputFeatureNames is not actually a subset of outputFeatures.map(_columnName).
The resulting model may still have more output features than those required (i.e. for ClassificationRowModel implementations which always have the same output features, or for OneHotEncodingModel whose output features naturally group together).
names of the output features that the new model should produce.
A model which has the same functionality for those output features listed, but potentially fewer input features.
Representation of the classical linear regression model y = intercept + a_0 x_0 + a_1 x_1 + ... + a_n x_n.
The length of the coefficients must match the length of the inputFeatures.
We use java.lang.Double for the type of the coefficients, because the scala Double type information is lost by scala/Gson and the deserialization fails badly for edge cases (e.g. Double.NaN).
Vector of coefficients of the model. Must match the length of inputFeatures.
Description of the (numeric) input features. Must match the length of coefficients.
Intercept of the model (defaults to 0).
Name used to identify the dependent feature in an evaluation dataset.
Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.