Day137 - STAT Review: Classification (7)
Practical Statistics for Data Scientists: Strategies for Imbalanced Data (1) (Undersampling & Oversampling)
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Day136 - STAT Review: Classification (6)
Practical Statistics for Data Scientists: Evaluating Classification Models (Confusion Matrix, ROC-AUC & Lift)
Day135 - STAT Review: Classification (5)
Practical Statistics for Data Scientists: Logistic Regression (3) Assessing the Model
Day134 - STAT Review: Classification (4)
Practical Statistics for Data Scientists: Logistic Regression (2) (GLM, Interpretation, Fitting the Model)
Day133 - STAT Review: Classification (3)
Practical Statistics for Data Scientists: Logistic Regression (1) (Mathematical Foundation: Odds, Logit Function, Formula, and Examples)
Day132 - STAT Review: Classification (2)
Practical Statistics for Data Scientists: Discriminant Analysis (Covariance, Discriminant Function, and Application: Predicting Default Risk)
Day131 - STAT Review: Classification (1)
Practical Statistics for Data Scientists: Naive Bayes (Theoretical Approach, Code Source, & Prediction)
Day130 - STAT Review: Regression and Prediction (8)
Practical Statistics for Data Scientists: Weighted Regression, and Interactions and Main Effects in Regression in Depth
Day129 - STAT Review: Regression and Prediction (7)
Practical Statistics for Data Scientists: Stepwise Regression & Model Selection in Depth
Day128 - STAT Review: Revisiting Mathematics Theories (4)
Mathematical Principles: Non-parametric Inference (Wilcoxon Signed-Rank Test & Wilcoxon Rank-Sum Test)