How-To
TreeNet® Classification
Before you start
Overview
Data considerations
Example
Example of discover key predictors
Example of prediction
Perform the analysis
Enter your data
Predictor elimination
Specify the class weights
Specify the interactions
Specify the validation method
Select the analysis options
Select the graphs to display
Select the results to display
Store statistics
Select an alternative model
Select an alternative model from Discover Key Predictors
Tune hyperparameters
Select hyperparameter values to evaluate
Add partial dependence plots
Select more predictors to plot
Predict new results
Predict new results
Select the prediction results to display
Store prediction statistics
Interpret the results
Method table
Response information table
Model evaluation
Optimization of hyperparameters
Average negative log-likelihood vs number of trees plot
Area under ROC curve vs number of trees plot
Misclassification rate vs number of trees plot
Model summary table
Relative variable importance chart
Top 2-way interaction strength tables
Confusion matrix
Misclassification table
Receiver operating characteristic (ROC) curve
Gain chart and Lift chart
Boxplot of event probabilities
Partial dependence plots
Prediction table
Methods and formulas
Methods
Selection of the optimal number of trees
Response information
Predictor elimination
Tune hyperparameters
Fits and probabilities
Model summary
Confusion matrix
Misclassification table
Top 2-way interaction strength
Receiver Operating Characteristic (ROC) curve
Gain chart
Cumulative lift chart
Lift chart
Partial dependence plots