ML_LifeExpectancy_Imbalanced_Classification

Trained the below models to classify the countires whose life expectancy is below 50 years the best model is chosen based on the mean cv score and scoring metric used is F1 scoring

Classification Task:

1. Basic Algorithms

   1. Naive Algorithm

   2. Accuracy: Predict the majority class (class 0).

   3. G-Mean: Predict a uniformly random class.

   4. F-Measure: Predict the minority class (class 1).

   5. ROC AUC: Predict a stratified random class.

   6. PR ROC: Predict a stratified random class.

   7. Brier Score: Predict majority class prior.

2. Logistic Regression

   3. Decision Tree
   4. k-Nearest Neighbors
   5. Support Vector Machine
   6. Random Forest
   7. Extra Trees
   8. Gradient Boosting
   9. XgBoost
   10. Stacking Classifiers

3. Cost Sensitive Algorithms 11. Logistic Regression 12. Decision Trees 13. Support Vector Machines 14. Random Forest 15. XGBoost 16. Extra Trees 17. Bagging decision tree with under sampling

4. Data Sampling Algorithms (pick one under sampling/oversampling)

   18. Logistic Regression
   19. Decision Tree
   20. k-Nearest Neighbors
   21. Support Vector Machine
   22. Random Forest
   23. Easy Ensemble Classifier
   24. XgBoost
   25. Stacking Classifiers