# ML Lab VTU

## Entropy and Information Gain in Decision Tree Learning

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 …

## Appropriate Problems For Decision Tree Learning

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: 1. Instances are represented by attribute-value pairs. “Instances are described by a fixed set of attributes (e.g., …

## Decision Tree Representation in Machine Learning

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 …

## 18CSL76 Artificial Intelligence Machine Learning Laboratory

18CSL76 Artificial Intelligence and Machine Learning Laboratory – VTU AIML Lab and Theory 18CS71 Artificial Intelligence and Machine Learning Laboratory – 18CSL76 (VTU AIML Lab) covers the different algorithms such as A* Search, A** Search, Find-S algorithms, Candidate elimination algorithm, Decision tree (ID3) algorithm, Artificial Neural Networks, Backpropagation Algorithm, Naïve Bayes classifier for text classification, …

## Backpropagation Algorithm Machine Learning

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 …

## Locally Weighted Regression Algorithm in Python

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 …

## k-Nearest Neighbour Algorithm in Python

Python Program to Implement the k-Nearest Neighbour Algorithm Exp. No. 9.   Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this problem. K-Nearest Neighbor Algorithm Training algorithm: For each training example (x, f (x)), add the …

## K-Means and EM Algorithm in Python

Python Program to Implement the K-Means and Estimation & MAximization Algorithm Exp. No. 8.   Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using the k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python …

## Bayesian network in Python using pgmpy

Python Program to Implement the Bayesian network using pgmpy Exp. No. 7.  Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using a standard Heart Disease Data Set. You can use Java/Python ML library classes/API. Theory A Bayesian network is a directed acyclic …

## Naïve Bayesian Classifier in Python using API

Python Program to Implement the Naïve Bayesian Classifier using API for document classification Exp. No. 6.  Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to perform this task. Built-in Java classes/API can be used to write the program. Calculate the accuracy, precision, and recall for your data …