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

  Machine Learning MCQs 👉1.  Machine Learning MCQs - UNIT 1 👉2.  Machine Learning MCQs-2 (Performance Metrics, Linear Regression, Naïve Bayes Classifier ) 👉3.  Machine Learning MCQs-3 (Logistic Regression, KNN, SVM, Decision Tree) 👉4.  Machine Learning MCQs - 4 (Clustering, Dimensionality Reduction) 👉5. Machine Learning MCQs - 5 (Ensemble Models)

  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

  Decision Tree MCQs 1.      Decision Trees can be used for A.     Classification Tasks B.     Regression Tasks C.     Multi-output tasks D.     All of the above Ans: D   2.      The iris dataset has A.     5 features and 3 classes B.     4 features and 3 classes C.     2 features and 3 classes D.     4 features and 2 classes Ans: B   3.      A node’s value attribute tells you how many training instances of each class this node applies to Top of Form  True  False Ans: A 4.      A node’s gini attribute measures Top of Form  The number of training instances in the node  The ratio of training instances in the node  Its impurity None of these Ans: C 5.      If all the training instances of a node belong to the same class then the value of the node's Gini attribute will be  1  0  Any integer between 0 and 1  A negative value Ans: B 6.      A Gini coefficient of 1 expresses maximal inequality amo