integration of various techniques to perform feature engineering
#install #!pip install --upgrade category_encoders #!pip install boruta #!pip install borutashap #!pip3 install catboost #!pip install eif #!pip install h2o
1.Select the top n features based on absolute correlation with train_target variable Function to calculate Cramer's V
2.Overall Correlation Function
3. Select top features based on information value
4. Select the top n features based on absolute value of beta coefficient of features
5. Select the features identified by Lasso regression
6. Select features based on Recursive Feature Selection method
7. Select features based on Sequential Feature Selector
8. Select features based on BorutaPy method
9. Select features based on BorutaShap method
10. Select features based on Forward selection method
11. Select features based on backward elimination method
12. Select features based on Bi-directional elimination method
13. Linear regression feature importance
14. Logistic Regression Feature Importance
15. CART Regression Feature Importance
16. CART Classification Feature Importance
17. Random Forest Regression Feature Importance
18. Random Forest Classification Feature Importance
19. XGBoost Regression Feature Importance
20. XGBoost Classification Feature Importance
21. Permutation Feature Importance for Regression
22. Permutation Feature Importance for Classification
23. Feature Selection with Importance