What is the core idea behind Machine Learning? A. To build advanced machines capable of performing complex calculations and tasks* B. To create models that can learn from data and improve performance over time C. To develop algorithms that can represent and explain data D. To accurately classify data into certain categories

What is the most common regularization technique in Machine Learning? A. Cross validation* B. K-Fold validation C. Early Stop regularization D. L2 regularization

What is the main issue that regularization techniques address in Machine Learning? A. Missing data B. Overfitting* C. Underfitting D. Feature selection

What is the purpose of linear regression algorithm? A. To accurately classify data into certain categories B. To generate a set of predictions C. To develop algorithms that can represent and explain data D. To establish relationships between independent variables and dependent variables*

What type of supervised learning technique is logistic regression? A. Unsupervised learning B. Anomaly detection C. Reinforcement learning D. Classification*

What is one of the main characteristics of decision trees? A. They can be used with both numerical and categorical data B. They use exponential growth to find the best way to represent data C. They are fast and require very little storage* D. They accurately classify data into certain categories

What is the process of separating data points with the largest possible margin while minimizing misclassification errors? A. Logistic regression B. Cross validation C. Decision Trees D. Support vector machines*

How can we detect overfitting in the models? A. By comparing the training and testing error* B. By comparing different models C. By using L2 regularization D. By using K-Fold validation

What is the main purpose of using feature selection techniques in Machine Learning? A. To reduce overfitting B. To reduce the computational complexity of the model* C. To reduce the number of features in the data D. To improve accuracy and reduce bias

What kind of supervised learning problem is linear regression used to solve? A. Classification B. Regression* C. Anomaly detection D. Clustering