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ML 1.10 ID 3 ALGORITHM

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  v   ID3 Algorithm: - Ø ID3 or  Iterative Dichotomiser3 Algorithm is used in machine learning for building decision trees from a given dataset. Ø It was developed in 1986 by Ross Quinlan. Ø It is a greedy algorithm that builds a decision tree by recursively partitioning the data set into smaller and smaller subsets until all data points in each subset belong to the same class. Ø It employs a top-down approach, recursively selecting features to split the dataset based on information gain. Ø It is used for both classification and regression tasks. Ø ID3 deals primarily with categorical properties, which means that it can efficiently handle objects with a discrete set of values. This property is consistent with its suitability for problems where the input features are categorical rather than continuous.   Ø The primary purpose of the ID3 algorithm is to construct a decision tree for classification tasks. 1.   ...
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ML 1.9 DECISION TREE MODEL OF LEARNING

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Decision Tree of Learning A Decision Tree is a supervised learning model used for classification and regression . A decision tree learns by splitting data into smaller and purer groups using simple rules , until it can confidently make a prediction. It makes decisions by splitting data into subsets based on feature values, forming a tree-like structure of rules. It learns by asking a sequence of simple questions about the data and splitting it step by step, like a flowchart, until it reaches a decision. Key Components of a Decision Tree: ·         Root Node : The topmost node representing the entire dataset, which is then split into subsets. ·         Decision Nodes : Intermediate nodes that represent decisions based on specific features, leading to further splits. ·         Leaf Nodes (Terminal Nodes) : Nodes that represent the final output or decision, containing no furthe...
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