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Sklearn Gradient Boosting

Gradient Boosting classifier and regressor using scikit-learn

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Predict

AI-generated

Summary

Make predictions using a trained Gradient Boosting model (classifier or regressor) via scikit-learn.

Inputs

  • Input Data (required) — Data items containing the feature columns specified in the Feature Columns parameter.

Output shape

Each input item is passed through with added fields: prediction (predicted value or class label) and, for classifiers, probabilities (array of class probabilities). Original fields are retained.

For regressors only prediction is added; for classifiers both prediction and probabilities are added.

Examples

Example 1: Predict house prices with a trained regressor

Model Type = Regressor, Model Data = , Feature Columns = 'sqft,bedrooms,baths', Python Path = 'python3'

Example 2: Classify email spam with a trained classifier

Model Type = Classifier, Model Data = , Feature Columns = 'word_count,link_count', Python Path = 'python3'

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