Q. In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea
✅ Correct Answer: (A)
TRUE
Explanation: In machine learning, instability refers to the sensitivity of an algorithm to changes in the training data. When an algorithm is unstable, small variations in the training data can lead to significant changes in the learned classifiers. Bagging, which stands for Bootstrap Aggregating, is a technique that aims to reduce the variance and improve the stability of machine learning models.