[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)
