Explain the concepts of Entropy and Information Gain in Decision Tree Learning. While constructing a decision tree, the very first question to be answered is, Which Attribute Is the Best Classifier? The central choice in the ID3 algorithm is selecting which attribute to test at each node in the tree. We would like to select […]
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What are appropriate problems for Decision tree learning? Although a variety of decision-tree learning methods have been developed with somewhat differing capabilities and requirements, decision-tree learning is generally best suited to problems with the following characteristics: Video Tutorial 1. Instances are represented by attribute-value pairs. “Instances are described by a fixed set of attributes (e.g.,
What are decision tree and decision tree learning? Explain the representation of the decision tree with an example. Decision Trees is one of the most widely used Classification Algorithm Features of Decision Tree Learning Method for approximating discrete-valued functions (including boolean) Learned functions are represented as decision trees (or if-then-else rules) Expressive hypotheses space, including
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Perspectives and Issues in Machine Learning Following are the list of issues in machine learning: 1. What algorithms exist for learning general target functions from specific training examples? In what settings will particular algorithms converge to the desired function, given sufficient training data? Which algorithms perform best for which types of problems and representations? 2.
Consistent Hypothesis, Version Space and List then Eliminate Algorithm Consistent Hypothesis The idea: output a description of the set of all hypotheses consistent with the training examples (correctly classify training examples). Video Tutorial of Consistent Hypothesis, Version Space, and List then Eliminate Algorithm Version Space Version Space is a representation of the set of hypotheses
AO* Search (And-Or) Graph, Advantages and Disadvantages – Artificial Intelligence – Artificial Intelligence The Depth-first search and Breadth-first search given earlier for OR trees or graphs can be easily adopted by AND-OR graph. The main difference lies in the way termination conditions are determined since all goals following an AND node must be realized; whereas
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A Star ( A*) Search Algorithm, Advantages and Disadvantages – Artificial Intelligence – Artificial Intelligence A* is a cornerstone name of many AI systems and has been used since it was developed in 1968 by Peter Hart; Nils Nilsson and Bertram Raphael. It is the combination of Dijkstra’s algorithm and the Best first search. It
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Best-First Search Algorithm – Artificial Intelligence – Artificial Intelligence We have studied two uninformed search algorithms such as Breadth-First Search (BFS) and Depth First Search (DFS) Algorithm. DFS is good because it allows a solution to be found without all competing branches having to be expanded. BFS is good because it does not get branches
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Simplest Hill-Climbing and Steppest Hill-Climbing Search Algorithms – Artificial Intelligence In hill climbing the basic idea is to always head towards a state which is better than the current one. So, if you are in town A and you can get to town B and town C (and your target is town D) then you
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Generate and Test Heuristic Search – Artificial Intelligence The generate-and-test strategy is the simplest of all the approaches. It consists of the following steps: Algorithm: Generate-and-Test 1. Generate a possible solution. For some problems. this means generating a particular point in the problem space. For others, it means generating a path from a start state.
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