ML-Set2.5

Which of the following is not a type of Machine Learning?

• a) Supervised Learning

• b) Unsupervised Learning

• c) Reinforcement Learning

• d) Analytical Learning

Which of the following is a common application of Supervised Learning?

• a) Image Classification

• b) Clustering

• c) Anomaly Detection

• d) Dimensionality Reduction

What is the primary goal of Reinforcement Learning?

• d) To reduce the number of features in a dataset

• c) To group similar data points

• b) To learn a policy that maximizes cumulative reward

• a) To predict outcomes based on input data

Which of the following techniques is typically used in Unsupervised Learning?

• d) Linear Regression

• a) Decision Trees

• b) K-Means Clustering

• c) Support Vector Machines

A random variable is a function that maps outcomes of a random process to:

• a) Probabilities

• b) Real numbers

• c) Events

• d) Frequencies

What is the main difference between Supervised Learning and Unsupervised Learning?

• b) Unsupervised Learning is faster than Supervised Learning

• a) Supervised Learning uses labeled data, while Unsupervised Learning does not

• c) Supervised Learning is only used for classification tasks

• d) Unsupervised Learning requires more data than Supervised Learning

In the context of Machine Learning, what does 'overfitting' refer to?

• d) A model that uses too many features

• c) A model that is too simple to capture the underlying patterns

• b) A model that generalizes well to new data

• a) A model that performs well on training data but poorly on unseen data

Which of the following algorithms is commonly used for classification tasks in Supervised Learning?

• b) Linear Regression

• d) Principal Component Analysis

• c) Decision Trees

• a) K-Means Clustering

What is the purpose of feature scaling in Machine Learning?

• a) To increase the number of features

• b) To ensure that all features contribute equally to the distance calculations

• c) To reduce the dimensionality of the dataset

• d) To improve the interpretability of the model

Which of the following is a common method for evaluating the performance of a classification model?

• c) R-squared

• d) Silhouette Score

• b) Accuracy

• a) Mean Squared Error