KMeansModel
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package ml
The clusters of the model. These should have distinct names.
A seq of numeric feature descriptions describing the input to the model. * @param identifier Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.
The total set of classes that the predicted value can take on.
The total set of classes that the predicted value can take on.
The set of possible values for the predicted value.
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.
The clusters of the model.
The clusters of the model. These should have distinct names.
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.
identifier for the model.
A seq of numeric feature descriptions describing the input to the model.
A seq of numeric feature descriptions describing the input to the model. * @param identifier Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.
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.
A model representing results of the K-Means clustering algorithm. Each cluster is a vector in a fixed dimensional space, and the transformer assigns the input row to its closest cluster using the Euclidean (L2) distance.
The clusters should all be of the same dimension, which should be equal to the number of input features.
The clusters of the model. These should have distinct names.
A seq of numeric feature descriptions describing the input to the model. * @param identifier Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.