What Are Decision Trees | Decision Tree Algorithm With Python | Machine Learning | Great Learning

A decision tree is the most powerful and widely used tool in machine learning for classification and prediction. A Decision tree basically stands for flowchart like a tree structure, where each internal node denotes a test on an attribute and each branch represents an outcome of the test, and each leaf node which is known as terminal node holds a class label. We will be exploring what are Decision Trees at stretch in this video.

Decision Tree Analysis is a general, predictive modeling tool that has applications spanning a number of different areas. In general, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. It is one of the most widely used and practical methods for supervised learning. Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

Decision trees have two varieties: Classification Trees [Supervised] and Regression Trees. Classification Trees are used to apply to output variables that are categorical in nature, whereas, Regression Trees are used to apply to output variables that are numeric or continuous. In this tutorial, we have covered all the important topics such as What are Decision Trees, What are its features, Common Measures Of Impurity, and much more that will help you to understand this concept with ease
Following pointers will be covered in this video:
00:00:00 Introduction
00:01:12 What are Decision Trees?
00:09:39 Common Measures Of Impurity
00:38:28 Demo

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Видео What Are Decision Trees | Decision Tree Algorithm With Python | Machine Learning | Great Learning канала Great Learning