Tree Based Models in Machine Learning Simplified for Beginners | ML Tutorial | Data Science

Have an overview on tree-based models in ML, concepts like random forests, decision tree models and more with Priya Kadakia in this video on Tree-based models in ML. Join India's number one outcome-focused Data Science and Machine Learning Program now: https://bit.ly/31sXc8p

Tree-based is a family of supervised Machine Learning which performs classification and regression tasks by building a tree-like structure for deciding the target variable class or value according to the features. Tree-based models use a series of if-then rules to generate predictions from one or more decision trees.

In computational complexity, the tree model is the model of computation in which an algorithm is considered to be basically a decision tree, i.e., a sequence of queries or tests that are done adaptively, so the outcome of the previous tests can influence the test that is performed next.

Watch the video till the end to understand how they work, what role they play in machine learning, provide you with relevant examples to give you a better understanding and more. Make sure you watch the video till the end to make the most out of it.

The following topics are covered in this video:

0:00 - Introduction
1:20 - Tree-based models in machine learning
1:34 - Examples of ML
2:33 - What is a decision tree
4:15 - What is random forest
6:10 - Bragging & bootstrap aggregating
7:53 - What is boosting

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