[Feature Engineering, Statistical Inference, Statistical Classification] · Outlier Test (Grubbs's Test, Rosner's Test) · Feature Selection (Correlation Analysis, Correlation Feature Selection, Boruta Method) · Goodness of Fit and Normality Test (Shapiro-Wilk Test, Kolmogorov-Smirnov Test, Kullback-Leibler Divergence) · Sampling (Oversampling, Undersampling) · Decision Tree Learning (Recursive Partitioning, ID3 Algorithm, C4.5 Algorithm, C5.0 Algorithm) · k-Nearest Neighbors · Generalized Linear Model (Lasso Regularization, Elastic Net Regularization) · Ensemble Learning (Boosting, Random Forest) · Classification Evaluation Metrics (Precision and TPR Recall, TPR Sensitivity and TNR Specificity, F-Score, ROC Curve, MCC, Kappa)
