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01_​Spark_​MLlib_​Decision_​Tree

Spark MLlib decision tree

This workflow demonstrates the usage of the Spark MLlib Decision Tree Learner and Spark Predictor. It also demonstrates the conversion of categorical columns into numerical columns which is necessary since the MLlib algorithms only support numerical features and labels.

The workflow makes use of the Create Local Big Data Environment node to create a Spark context. You can swap this node out for a Create Spark Context (Livy) node to connect to a remote cluster.

Apache Spark MLlib Decision Tree This workflow demonstrates the usage of the Spark MLlib Decision Tree Learner and Spark Predictor. KNIME tableto DataFrameKNIME tableto DataFrametraining datatest data Spark CategoryTo Number Spark Decision TreeLearner (MLlib) Spark Predictor(MLlib) Spark Number ToCategory (Apply) Spark Scorer Table to Spark Table to Spark Create Local BigData Environment File Reader(Complex Format) File Reader(Complex Format) Apache Spark MLlib Decision Tree This workflow demonstrates the usage of the Spark MLlib Decision Tree Learner and Spark Predictor. KNIME tableto DataFrameKNIME tableto DataFrametraining datatest data Spark CategoryTo Number Spark Decision TreeLearner (MLlib) Spark Predictor(MLlib) Spark Number ToCategory (Apply) Spark Scorer Table to Spark Table to Spark Create Local BigData Environment File Reader(Complex Format) File Reader(Complex Format)

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