January 10, 2021
The purpose of stacking is to ensure that patterns in each subset of the training data have been properly learned. Let’s say that one of the base model constantly makes wrong predictions, then the meta model which receives output from it and other models(who do correct predictions) will also learn this and give less weightage to outputs of the aforementioned faulty model. Thus the learning process will be much more accurate.
by : Monis Khan
Quick Summary:
The purpose of stacking is to ensure that patterns in each subset of the training data have been properly learned. Let’s say that one of the base model constantly makes wrong predictions, then the meta model which receives output from it and other models(who do correct predictions) will also learn this and give less weightage […]