Machine Learning Algorithms MCQ Questions and Answers | Data Science MCQs

A. Linear Regression

B. K-Means Clustering

C. Decision Tree

D. Logistic Regression

A. The model performs well on the test data

B. The model fits the training data perfectly

C. The model fails to generalize

D. The model is too simple to learn patterns

A. Support Vector Machine (SVM)

B. K-Means Clustering

C. Random Forest

D. Linear Regression

A. To minimize errors on the training data

B. To create a linear regression model

C. To visualize data relationships

D. To classify data points into categories

A. Naive Bayes

B. K-Nearest Neighbors (KNN)

C. Random Forest

D. Linear Regression

A. The model's flexibility

B. The difference between predicted and actual values

C. The model's ability to generalize

D. The model's ability to fit the training data

A. Gradient Boosting

B. Principal Component Analysis (PCA)

C. K-Means Clustering

D. Logistic Regression

A. To maximize the margin between classes

B. To minimize the number of support vectors

C. To visualize data relationships

D. To perform unsupervised learning

A. K-Nearest Neighbors (KNN)

B. Decision Tree

C. Naive Bayes

D. Linear Regression

A. It is highly interpretable

B. It is computationally efficient

C. It reduces overfitting

D. It requires a large amount of training data