ML Lab VTU

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

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

Backpropagation Algorithm – Machine Learning – Artificial Neural Network In this tutorial i will discuss the Backpropagation Algorithm and its implementation in Python. Video Tutorial on Backpropagation Algorithm BACKPROPAGATION (training_example, ƞ, nin, nout, nhidden) Each training example is a pair of the form (𝑥, 𝑡), where (𝑥) is the vector of network input values, and

Python Program to Implement the Locally Weighted Regression Algorithm Exp. No. 10.   Implement the non-parametric Locally Weighted Regression algorithm in Python in order to fit data points. Select the appropriate data set for your experiment and draw graphs. Locally Weighted Regression Algorithm Regression: Regression is a technique from statistics that are used to predict values