list of class labels, ranging from 0 to (C - 1) inclusive
log of class priors, whose dimension is C, number of labels
log of class conditional probabilities, whose dimension is C-by-D, where D is number of features
Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.
Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.
Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.
Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.
Chains an estimator onto the end of this pipeline, producing a new pipeline.
Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
Chains an estimator onto the end of this pipeline, producing a new pipeline.
Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
Chains a pipeline onto the end of this one, producing a new pipeline.
Chains a pipeline onto the end of this one, producing a new pipeline. If either this pipeline or the following has already been executed, it will not need to be fit again.
the pipeline to chain
Transforms a feature vector to a vector containing the log(posterior probabilities) of the different classes according to this naive bayes model.
Transforms a feature vector to a vector containing the log(posterior probabilities) of the different classes according to this naive bayes model.
The input feature vector
Log-posterior probabilites of the classes for the input features
The application of this Transformer to an RDD of input items.
The application of this Transformer to an RDD of input items. This method may optionally be overridden by ML developers.
The bulk RDD input to pass into this transformer
The bulk RDD output for the given input
list of class labels, ranging from 0 to (C - 1) inclusive
log of class priors, whose dimension is C, number of labels
log of class conditional probabilities, whose dimension is C-by-D, where D is number of features
A method that converts this object into a Pipeline.
A method that converts this object into a Pipeline. Must be implemented by anything that extends Chainable.
A Multinomial Naive Bayes model that transforms feature vectors to vectors containing the log posterior probabilities of the different classes