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In this video on Decision Tree ID3 algorithm, We’ll take you through the basics of the ID3 algorithm, a method used to create decision trees, which are powerful tools for making data-driven decisions. This video on decision tree, we’ll break down the ID3 algorithm in simple terms. You'll learn how it builds decision trees by selecting the best questions to ask at each step, using concepts like entropy and information gain. We’ll also show you a practical example, so you can see how the ID3 algorithm works with real data.
By the end of this video, you'll have a clear understanding of how the ID3 algorithm works and how you can apply it to make smart decisions with your data
Below are the topics covered in this ID3 algorithm video:-
✅What is the decision tree ID3 algorithm?
Ans: ID3 (Iterative Dichotomiser 3) is an algorithm developed by Ross Quinlan for generating decision trees from datasets. It serves as the predecessor to the C4.5 algorithm and is commonly applied in machine learning and natural language processing. This process results in a decision tree generated by ID3.
✅What is a decision tree and its algorithm?
Ans: A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes.
✅Which algorithm is used for decision tree induction?
Ans: Some of the most well-known algorithms include: ID3 (Iterative Dichotomiser 3): This algorithm uses entropy and information gain to build a decision tree for classification tasks.
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