Weights computed for every feature.
Intercept computed for this model.
Current version of model save/load format.
Current version of model save/load format.
Intercept computed for this model.
Intercept computed for this model.
Predict values for examples stored in a JavaRDD.
Predict values for examples stored in a JavaRDD.
JavaRDD representing data points to be predicted
a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
Predict values for a single data point using the model trained.
Predict values for a single data point using the model trained.
array representing a single data point
Double prediction from the trained model
Predict values for the given data set using the model trained.
Predict values for the given data set using the model trained.
RDD representing data points to be predicted
RDD[Double] where each entry contains the corresponding prediction
Predict the result given a data point and the weights learned.
Predict the result given a data point and the weights learned.
Row vector containing the features for this data point
Column vector containing the weights of the model
Intercept of the model.
Save this model to the given path.
Save this model to the given path.
This saves:
The model may be loaded using Loader.load
.
Spark context used to save model data.
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.
Export the model to a String in PMML format
Export the model to a String in PMML format
Export the model to the OutputStream in PMML format
Export the model to the OutputStream in PMML format
Export the model to a directory on a distributed file system in PMML format
Export the model to a directory on a distributed file system in PMML format
Export the model to a local file in PMML format
Export the model to a local file in PMML format
Print a summary of the model.
Print a summary of the model.
Weights computed for every feature.
Weights computed for every feature.
Regression model trained using Lasso.