Model Validation Operators

The Model Validation operators (Scoring operators) allow the modeler to assess whether a model is statistically valid and how well it is performing.

Note: Find these operators in the Predict section of Team Studio.
  • Alpine Forest Evaluator
    Provides model accuracy data, a confusion matrix heat map that illustrates the classification model's accuracy for each possible predicted value, and an error convergence rate graph.
  • Classification Threshold Metrics
    Use to output (binary or multi-class) classification performance metrics for different confidence thresholds associated with a unique class that the user specifies.
  • Confusion Matrix
    Displays information about actual versus predicted counts of a classification model and helps assess the model's accuracy for each of the possible class values.
  • Goodness of Fit
    Verifies a trained model.
  • Lift (DB)
    Visualizes the performance of a classification model. It applies in general to classification models (for example, CART, Decision Tree, Logistic Regression, Naive Bayes, Neural Network, and Alpine Forest).
  • Lift (HD)
    Visualizes the performance of a classification model. It applies in general to classification models (for example, CART, Decision Tree, Logistic Regression, Naive Bayes, Neural Network, and Alpine Forest).
  • Regression Evaluator (DB)
    Computes several commonly used statistical tests to determine the accuracy of several columns (Predicted Values). These represent predictions against one column (Actual Value), which is specified as the "ground truth."
  • Regression Evaluator (HD)
    Computes several commonly used statistical tests to determine the accuracy of several columns (Predicted Values). These represent predictions against one column (Actual Value) which is specified as the "ground truth."
  • ROC
    Generates a Receiver Operating Characteristic (ROC), or ROC curve.
  • T-Test - Independent Samples
    Computes a test of statistical significance against a student's t-distribution for one measure across two different groups.
  • T-Test - Paired Samples
    Computes a test of statistical significance for two measures of the same data points. This is the same as computing a single sample t-test against the difference between the two columns and a known mean of zero.
  • T-Test - Single Sample
    Tests for statistical significance between a set of numeric values (from one column) and a known mean. This operator allows one to compute the test across several different sample columns with one operator.
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