Question 1

What is the purpose of dimensionality reduction in machine learning?

Options :

A : Data Augmentation

B : Model Evaluation

C : Model Regularization

D : Feature Extraction

E : Model Interpretation

F : Noise Reduction

Answer: D

Question 2

Which of these is a benefit of using convolutional neural networks (CNNs) for image processing?

Options :

A : They require smaller datasets

B : They are less computationally intensive

C : They automatically detect important features

D : They do not require labeled data

Answer: C

Question 3

Which of the following is NOT a common technique for improving the performance of classification models?

Options :

A : Feature Engineering

B : Ensemble Learning

C : Hyperparameter Tuning

D : Regularization

E : Gradient Descent

F : Dimensionality Reduction

Answer: E

Question 4

What is the main purpose of using activation functions in neural networks?

Options :

A : To normalize input data

B : To introduce non-linearity

C : To reduce dimensionality

D : To cluster data

Answer: B

Question 5

Which of the following is an example of supervised learning?

Options :

A : K-means

B : Unsupervised Learning

C : Linear Regression

D : Linear Regression

Answer: D