Ensemble techniques/Cascade Models- Best Practices

ensemble_methods
classification
accuracy

#1

I am building a two step Machine Learning Model where the output (it’s a score/rating) of lower level ML model is used as an input to another ML model. I was wondering if experts can share some insights on what the best ways I can minimize the error of this combination or point to a relevant video/link.
To give a context- I am working on a ML Churn prediction model for sales which predicts if a customer will renew a contract or not based on certain factors, some of these factors like engagement scores are derived from another ML algorithm. I wanted to understand what are my options here to improve accuracy and reduce error …or overall what are the industry’s best practices in combing different ML models.

Thanks in Advance!


#2

Hi @ravindran.dandala
The stacking ensemble that many Kaggle peopler use:
Ensemble
Other way read the interviews of the top competitors in Kaggle they usually describe their final solution.
Best regards
Alain