Stacking Classifier | Ensemble Classifiers | Machine Learning

Stacking is an ensemble learning technique to combine multiple classification models via a meta-classifier. The individual classification models are trained based on the complete training set; then, the meta-classifier is fitted based on the outputs -- meta-features -- of the individual classification models in the ensemble. The meta-classifier can either be trained on the predicted class labels or probabilities from the ensemble.

Let's first understand how a stacking classifier works and create a simple stacking classifier in Python.

If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.

If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful.

Be sure to subscribe for future videos & thank you all for watching.

You can find me on:
GitHub - https://github.com/bhattbhavesh91
Medium - https://medium.com/@bhattbhavesh91
#stackingclassifier #ensemble #metaclassifier

Видео Stacking Classifier | Ensemble Classifiers | Machine Learning канала Bhavesh Bhatt