Spark Logistic Regression Learner

Node / Learner

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.

Node details

Ports Options Views

Input ports

Type: Spark Data

Input data

Input Spark DataFrame with training data.

Output ports

Type: Spark ML Model

Spark ML linear learner model (regression)

Spark ML linear learner model (regression)
Type: Table

Coefficients and Intercept

Coefficients and Intercept of the logistic regression model.
Type: Table

Accuracy Statistics

Accuracy statistical measures of the learned regression model, when applied to the training dataset

Extension

The Spark Logistic Regression Learner node is part of this extension:

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