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Sklearn Voting Classifier

Ensemble voting classifier combining multiple models

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Summary

Train ensemble classifier using multiple scikit-learn models on specified feature and target columns.

Inputs

  • featureColumns (required) — Comma-separated list of feature column names
  • targetColumn (required) — Name of the target column
  • estimators — Select estimators to include in the ensemble
  • voting — Voting strategy (hard or soft)
  • pythonPath — Path to Python executable with scikit-learn installed

Output shape

a single record containing model metadata and training metrics

Returned object includes base64-encoded model pickle, training score, voting method, list of estimators, class labels, feature columns, and training sample count.

Examples

Example 1: Train on dataset iris.csv using logistic regression, random forest, and Gaussian NB with soft voting

featureColumns='sepal_length,sepal_width', targetColumn='species', estimators=['LogisticRegression','RandomForestClassifier','GaussianNB'], voting='soft'

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