import org.apache.spark.ml.regression._
val glm = new GeneralizedLinearRegression()
import org.apache.spark.ml.linalg._
val features = Vectors.sparse(5, Seq((3,1.0)))
val trainDF = Seq((0, features, 1)).toDF("id", "features", "label")
val glmModel = glm.fit(trainDF)GeneralizedLinearRegression (GLM)
GeneralizedLinearRegression is a regression algorithm. It supports the following error distribution families:
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gaussian -
binomial -
poisson -
gamma
GeneralizedLinearRegression supports the following relationship between the linear predictor and the mean of the distribution function links:
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identity -
logit -
log -
inverse -
probit -
cloglog -
sqrt
GeneralizedLinearRegression supports 4096 features.
The label column has to be of DoubleType type.
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Note
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GeneralizedLinearRegression belongs to org.apache.spark.ml.regression package.
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GeneralizedLinearRegression is a Regressor with features of Vector type that can train a GeneralizedLinearRegressionModel.
Regressor
Regressor is a custom Predictor.