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In the family of supervised learning algorithms, there are many techniques to perform the classification or regression over the training set. The machine learning technique that we apply to solve a problem often result in a single model. We are basically dependent on this model for all our results. The best we can do is to tune the hyper-parameters.

But what if we could combine different types of models and arrive at an ensemble model which takes care of the weak areas of the single model?

With the concept of decision trees and the concept of ensemble in machine learning…

Shubham Soni

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