NodeH2O Random Forest Learner

Learner

Learns a Distributed Random Forest (DRF) classification model using H2O .

Input ports
  1. H2O training frame. Type: H2O Frame
    H2O Frame with training data.
Output ports
  1. Variable importance measures Type: Data
    Variable importance in tabular format.
  2. H2O DRF classification model. Type: H2O Model
    H2O Distributed Random Forest classification model.
  1. Classification_Random Forest usig H2O
    Random Forest nased H2O- The workflow repated n times.
    malik > Public > Classification_RF_H2O
  2. Guided Automation
    autoML guided analytics automated machine learning guided automation feature engineering parameter optimization hyperparameter hyper-parameter optimization feature selection WebPortal application web app machine learning
    This workflow generates a fully automated web based application to select, train, test, and optimize a number of machine…
    knime > Examples > 50_Applications > 36_Guided_Analytics_for_ML_Automation > 01_Guided_Analytics_for_ML_Automation > 01_Guided_Analytics_for_ML_Automation
  3. h2o_rndfor
    knime > Examples > 50_Applications > 36_Guided_Analytics_for_ML_Automation > 01_Guided_Analytics_for_ML_Automation > Models > classification > h2o_rndfor
  4. H2O Cross-Validation
    H2O machine learning cross-validation
    This workflow shows how to use cross-validation in H2O using the KNIME H2O Nodes. In the example we use the H2O Random F…
    knime > Examples > 04_Analytics > 15_H2O_Machine_Learning > 04_H2O_Crossvalidation
  5. Combine Big Data, Spark and H2O.ai Sparkling Water
    - load data into (local) big data environment - load data into Spark context - load data into H2O.ai Sparkling Water con…
    mlauber71 > Public > kn_example_h2o_sparkling_water