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Spark Logistic Regression Learner

Tools & Services Apache Spark Mining Prediction
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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

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

Extension

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

Related workflows & nodes

  1. Logistic Regression with Spark
    Classification Machine learning Prediction
    +5
    This workflow is builds a classification model using logistic regression in SPARK.
    knime > Examples > 10_Big_Data > 02_Spark_Executor > 15_Logistic_Regression_with_Spark
    knime