Dr. Rambabu Pemula

  MACHINE LEARNING USING PYTHON LAB Course Outcomes: At the end of the course the student should be able to: 1.      Select data, model selection, model complexity and identify the trends. 2.      Understand a range of machine learning algorithms along with their strengths and weaknesses. 3.      Build predictive models from data and analyze their performance. List of Experiments WEEK 1 : a)      Write a program using scikit-learn to implement K-means Clustering b)      Program to calculate the entropy and the information gain c)      Program to implement perceptron. WEEK 2: ·        Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file. WEEK 3: ·        Fo...

Q.  Decision Tree Classification Problem Description As you know, Decision Tree is all about splitting nodes at different levels and trying to classify accurately as much as possible. You are given a feature (1-d array) and label (1-d array) (target) where you have to determine which value in the corresponding feature is best to split upon at the first root level for building a decision tree. The feature would be having continuous values whereas the target is binary in nature. So, The main task is to determine which value/threshold is best to split upon considering the classification task taking the loss as entropy and maximizing Information Gain. Input Format Two inputs: 1. 1-d array of feature 2. 1-d array of label Output Format Return threshold value Example Input feature: [0.58 0.9  0.45 0.18 0.5  0.12 0.31 0.09 0.24 0.83] label: [1 0 0 0 0 0 1 0 1 1] Example Output 0.18 Example Explanation If you calculate Information Gain for all of the feature values, it would be c...