1 Which of the following statements is false about Ensemble learning?

a) It is a supervised learning algorithm
b) It is an unsupervised learning algorithm
c) More random algorithms can be used to produce a stronger ensemble
d) Ensembles can be shown to have more flexibility in the functions they can represent

Correct Answer – B

2.Which of the following statements is true about stochastic gradient descent?
a) It processes one training example per iteration
b) It is not preferred, if the number of training examples is large
c) It processes all the training examples for each iteration of gradient descent
d) It is computationally very expensive, if the number of training examples is large

Correct Answer – A

3 Decision tree uses the inductive learning machine learning approach.
a) False
b) True

Correct Answer – B

4 What elements describe the Candidate-Elimination algorithm?
a) depends on the dataset
b) just a set of candidate hypotheses
c) just a set of instances
d) set of instances, set of candidate hypotheses

Correct Answer – D

5 Which of the following statements is not true about boosting?
a) It mainly increases the bias and the variance
b) It tries to generate complementary base-learners by training the next learner on the mistakes of the previous learners
c) It is a technique for solving two-class classification problems
d) It uses the mechanism of increasing the weights of misclassified data in preceding classifiers

Correct Answer – A

6. Which of the following is a core component of Machine Learning?

a) Statistical analysis
b) Data preprocessing
c) Feature engineering
d) All of the above

Correct Answer – D

7 What is overfitting in machine learning?

a) Model performs well on both training and testing data
b) Model performs poorly on training data but well on testing data
c) Model performs exceptionally well on training data but poorly on testing data
d) Model does not learn from data

Correct Answer – C

8 Which of the following is NOT an example of supervised learning?

a) Linear regression
b) Support Vector Machine (SVM)
c) K-means clustering
d) Decision trees

Correct Answer – C

9 In Deep Learning, which of the following is a typical activation function?

a) Linear function
b) Sigmoid function
c) Exponential function
d) Quadratic function

Correct Answer – B

10 Which of the following is the key difference between Machine Learning and Deep Learning?

a) Deep Learning requires large datasets and high computational power
b) Machine Learning requires large datasets and high computational power
c) Deep Learning does not require labeled data
d) Machine Learning is always supervised

Correct Answer – A