Bagged Trees using Scikit-Learn (Python)

This is a free preview video from the Machine Learning with Scikit-Learn LinkedIn Learning Course: https://www.linkedin.com/learning/machine-learning-with-scikit-learn/effective-machine-learning-with-scikit-learn.
Code here: https://github.com/mGalarnyk/Python_Tutorials/blob/master/Sklearn/CART/Visualization/02_09_Bagged_Trees.ipynb.

Each machine learning algorithm has strengths and weaknesses. A weakness of decision trees is that they're prone over fitting on the training set. A way to mitigate this problem, is to constraint how large a tree can grow. Bagged trees try to overcome this weakness by using bootstrapped data, to grow multiple deep decision trees. Bagged trees are an ensemble model which means that many models each other from individual weaknesses. What this image thumbnail shows is that multiple decision trees come together to make a combined prediction. In this video, I'll share with you how you can build a Bagged Tree Model using Scikit-Learn.

Видео Bagged Trees using Scikit-Learn (Python) канала Michael Galarnyk