Ensemble techniques/Cascade Models- Best Practices



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!


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