Machine Learning

 Machine Learning Programs 👉 Data Preprocessing in Machine Learning 👉 Data Preprocessing in Machine learning (Handling Missing values ) 👉 Linear Regression - ML Program - Weight Prediction 👉 Naïve Bayes Classifier - ML Program 👉 LOGISTIC REGRESSION - PROGRAM 👉 KNN Machine Learning Program 👉 Support Vector Machine (SVM) - ML Program 👉 Decision Tree Classifier on Iris Dataset 👉 Classification of Iris flowers using Random Forest 👉 DBSCAN 👉 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 👉 For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples. 👉 Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and

  Decision Trees Characteristics Context: Decision Trees are a fundamental machine learning algorithm used for both classification and regression tasks. Understanding their characteristics, capabilities, and limitations is crucial for effectively applying them to solve real-world problems. Question: Which of the following statements are true regarding the properties and behavior of Decision Trees? Statements to Evaluate: 1. Decision tree makes no assumptions about the data. 2. The decision tree model can learn non-linear decision boundaries. 3. Decision trees cannot explain how the target will change if a variable is changed by 1 unit (marginal effect). 4. Hyperparameter tuning is not required in decision trees. 5. In a decision tree, increasing entropy implies increasing purity. 6. In a decision tree, the entropy of a node decreases as we go down the decision tree. Choose the correct answer from below : A) 1, 2, and 5 B) 3, 5 and 6 C) 2, 3, 4 and 5 D) 1,2,3 and 6 Ans: D 1, 2, 3 and 6

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 computed as : (