MACHINE LEARNING

Type I error in hypothesis testing is
(i) Accepting null when false
(ii) Rejecting null when true
(iii) Rejecting alternative when false
(iv) Accepting alternative when true

In Bagging, multiple models are trained on
(i) The same dataset
(ii) Only test data
(iii) Random subsets of the training data
(iv) Feature-reduced data only

Why is clustering important in machine learning? Show the steps of the k-means algorithm by taking a suitable example. Give the time complexity analysis of k-means.

Compare bagging, AdaBoost, and gradient boosting. Discuss how each method reduces bias and variance and improves model performance.

Write short note on: Hierarchical Clustering