Alpine Forest Operators

Team Studio provides modeling, evaluation, validation, and prediction operators for regression (continuous) or classification (categorical) machine learning applications.

Operator Data source type Type of modeling
Alpine Forest Regression Hadoop Applies an ensemble algorithm to make a numerical prediction by aggregating (majority vote or averaging) the numerical regression tree predictions of the ensemble. Applies to continuous (numerical) data.
Alpine Forest Classification Hadoop Applies an ensemble classification method of creating a collection of decision trees with controlled variation. Applies to categorical data.
Alpine Forest Evaluator Hadoop Provides model accuracy data that illustrates the classification model's accuracy for each possible predicted value, and an error convergence rate graph.
Alpine Forest - MADlib Database Applies a MADlib function to generate multiple decision trees, the combination of which is used to make a prediction based on several independent columns.
Alpine Forest Predictor - MADlib Database Uses the model trained by Alpine Forest (MADlib) and scores the results. It must be connected to an Alpine Forest (MADlib) operator.
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