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!