Random Forest Classification Details—Binomial

The Model Detail screen includes the following information for binomial Random Forest Classification models:

Metrics

Provides training, test, and n-fold data for the following:

  • Mean squared error (MSE)
  • Root mean squared error (RMSE)
  • Number of observations
  • R-squared (R2)
  • Logloss
  • Area under the curve (AUC)
  • Precision-recall area under the curve (PR AUC)
  • Gini
  • Mean per class error

Maximum Metrics Threshold

Provides the Training Maximum Metrics Threshold for training, test, and n-fold data using the following metrics:

  • max f1
  • max f2
  • max f0point5
  • max accuracy
  • max precision
  • max recall
  • max specificity
  • max absolute_mcc
  • max min_per_class_accuracy
  • max mean_per_class_accuracy

Confusion Matrix

Illustrates the performance of a model on a set of training, test, and n-fold data for which the true values are known.

Variable Importances

Provides importance values for each variable using the following metrics:

  • Relative importance
  • Scaled importance
  • Percentage

Also charts the top 25 variables in the model.

AUC Curves

Area under the curve; determines which of the used models predicts the classes best using training, test, and n-fold data.

Lift/Gain Curves

Evaluates the prediction ability of a binary classification model using training, test, and n-fold data.