Creating Classification model

From a workbook, click Actions > ML Dashboard. The ML Dashboard page is displayed. From the Create Model section, click Classification and specify the following:

Feature	Random Forest
Model Type	Ensemble tree-based model
Best Suited For	Complex, non-linear relationships; mixed data types
Relationship Modeling	Captures non-linear patterns via aggregated decision trees
Handling of Interactions	Automatically captures feature interactions
Interpretability	Moderate – feature importances available, but overall model is a “black box”
Parameter Tuning	Several hyperparameters (n_estimators, max_depth, min_samples_split, etc.)
Data Requirements	Works with high-dimensional, noisy data; tolerates outliers and missing values
Handling Imbalanced Data	Supports class weights or balanced subsampling
Feature Importance	Built-in via mean decrease impurity or permutation importance
Library Support	scikit-learn (RandomForestClassifier), XGBoost (RF variant), Spark MLlib
Prediction Output	Hard labels and class probabilities
Deployment Readiness	Scales well; fast inference per tree; model size grows with number of trees
External Features	Easy to add new features without changing core algorithm
Community & Resources	Extensive tutorials, academic papers, and widespread industry adoption

Click Create and commence the model training process.