Spark Linear Regression Learner

This node uses the spark.ml linear regression implementation to train a linear regression model in Spark, supporting different regularization options. The target column must be numeric, whereas the feature columns can be either nominal or numerical.

Use the Spark Predictor (Regression) node to apply the learned model to unseen data.

Please refer to the Spark documentation for a full description of the underlying algorithm.

This node requires at least Apache Spark 2.4.

Input ports

1. Type: Spark Data
   Input data
   Input Spark DataFrame with training data.

Output ports

1. Type: Spark ML Model
   Spark ML linear learner model (regression)
   Spark ML linear learner model (regression)
2. Type: Table
   Coefficients and Intercept
   Coefficients and statistics of the linear regression model.
3. Type: Table
   Model Statistics
   Statistical measures of the learned regression model, when applied to the training dataset (R², explained variance, ...)