Logistic regression, Lasso, XGBoost, random forest, support vector machines, k-nearest neighbors and decision trees.

Class balancing with SMOTE variants or ADASYN, feature selection from ANOVA to Boruta and MRMR, imputation, encoding, scaling and cross-validation.

Let AutoML search the options and rank the resulting models on a leaderboard.

SHAP values, permutation importance and coefficient importance show which variables drive each model.

ROC and precision-recall curves, calibration, confusion matrices, residuals and learning curves, side-by-side model comparison, and a self-contained report to download.