Spark Logistic Regression Learner

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

Use the Spark Predictor (Classification) 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 Intercept of the logistic regression model.
3. Type: Table
   Accuracy Statistics
   Accuracy statistical measures of the learned regression model, when applied to the training dataset