What can we get from Ensemble Project?
Motivations for this project includes:
- How to get a sample bagging or stacking model?
- How to tune the a large number of the params?
- How to deploy the trained models?
If you just want to use the methods i suggested with these codes ,you may read the article:
If you want to go deep into the methods i suggested with these codes ,you may read the article:
And to be honest,the second article is a little bit hard than the first one. For all that,i still suggest that all these two articles should be read Carefully.
- All these codes (Bagging_tuning、Stacking_gbdt_logistic_regression、Stacking_gbdt_logistic_regression) just show how to tuning a good ensemble model ,they may be not at the best params
- Be patient with the code annotation
- The input data structured by me randomly , it's meaningless
- Data_preprocessing first, you can skip as well ,it targets to transform the data and it was uploaded into the data folder already.
- Bagingg_tuning,Stacking_gbdt_logistic_regression or Stacking_xgboost_logistic_regression
- Deployment_with_trained_models at last, it shows how to deploy trained models and can be ignored as well
You can get them all at folder : machine_learning/data/ easily
The initial data , you need transfer them by the script Data_preprocessing.py
They be transfered by the initial data , you need train the ensemble models with them
We got them by training the model : Stacking_xgboost_logistic_regression , and deploy them with Deployment_with_trained_models.py
For some ulterior reasons, i skip some codes among the codes. U know why~:) Thank u for reading , wish u a nice start