What type of machine learning algorithm models the relationship between variables? A. Clustering B. Classification C. Regression D. Neural networks

Which of the following is used to predict a continuous target value? A. Decision Trees B. Support Vector Machines C. KNN D. Linear Regression

Which of the following is an example of supervised learning? A. K-means clustering B. Reinforcement learning C. Linear Discriminant Analysis D. Linear Regression

Which algorithm helps non-linear relationships between variables? A. Logistic Regression B. KNN C. Decision Trees D. Naive Bayes

Which of the following is an example of unsupervised learning? A. Linear Regression B. K-means Clustering C. Random Forests D. Naive Bayes

What is the process of reducing the number of features without losing important information? A. Feature Selection B. Feature Importance C. Feature Extraction D. Feature Scoring

What is the process of building a model to perform predictions on unseen data? A. Model Building B. Model Tuning C. Model Testing D. Model Evaluation

What is the use of a confusion matrix? A. To visualize the actual vs. predicted target classes B. To optimize the parameters of a model C. To evaluate the performance of a model D. To reduce the dimensionality of a dataset

What is the purpose of k-fold cross-validation? A. To reduce the bias of a model B. To optimize the hyperparameters of a model C. To estimate the performance of a model D. To select the best type of model

Which of the following describes the bias-variance tradeoff? A. Increasing model complexity increases bias and reduces variance B. Increasing model complexity reduces bias and increases variance C. Increasing model complexity increases bias and increases variance D. Increasing model complexity reduces bias and reduces variance