Day146 - STAT Review: Statistical Machine Learning (8)
Practical Statistics for Data Scientists: Boosting (1) (Key Concepts & XGBoost)
Recent posts
Day145 - STAT Review: Statistical Machine Learning (7)
Practical Statistics for Data Scientists: Bagging and the Random Forest (2) (Random Forest II & Variable Importance)
Day144 - STAT Review: Statistical Machine Learning (6)
Practical Statistics for Data Scientists: Bagging and the Random Forest (1) (Bagging and Random Forest)
Day143 - STAT Review: Statistical Machine Learning (5)
Practical Statistics for Data Scientists: Tree Models (3) (Dealing With Overfitting Problems in R and Python & Predicting a Continuous Value)
Day142 - STAT Review: Statistical Machine Learning (4)
Practical Statistics for Data Scientists: Tree Models (2) (A Simple Example, The Recursive Partitioning Algorithm, & Measuring Homogeneity or Impurity)
Day141 - STAT Review: Statistical Machine Learning (3)
Practical Statistics for Data Scientists: K-Nearest Neighbors (3) (KNN as a Feature Engine) & Tree Models (1) (Key Concepts)
Day140 - STAT Review: Statistical Machine Learning (2)
Practical Statistics for Data Scientists: K-Nearest Neighbors (2) (Standardization & Choosing K)
Day139 - STAT Review: Statistical Machine Learning (1)
Practical Statistics for Data Scientists: K-Nearest Neighbors (1) (Example, Distance Metrics, and One Hot Encoder)
Day138 - STAT Review: Classification (8)
Practical Statistics for Data Scientists: Strategies for Imbalanced Data (2) (Data Generation, Cost-Based Classification, and Exploring the Predictions)
Day137 - STAT Review: Classification (7)
Practical Statistics for Data Scientists: Strategies for Imbalanced Data (1) (Undersampling & Oversampling)