Generalized linear regression (GLR)

In an LR, the output is assumed to follow a Gaussian distribution. In contrast, in generalized linear models (GLMs), the response variable Y_i follows some random distribution from a parametric set of probability distributions of a certain form. As we have seen in the previous example, following and creating a GLR estimator will not be difficult:

val glr = new GeneralizedLinearRegression()
      .setFamily("gaussian")//continuous value prediction (or gamma)
      .setLink("identity")//continuous value prediction (or inverse)
      .setFeaturesCol("features")
      .setLabelCol("label")

For the GLR-based prediction, the following response and identity link functions are supported based on data types (source: https://spark.apache.org/docs/latest/ml-classification-regression.html#generalized-linear-regression...