Machine Learning and its Applications

You are given reviews of movies marked as positive, negative, and neutral. Classifying reviews of a new movie is an example of

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• None of these

The selling price of a house depends on many factors. For example, it depends on

the number of bedrooms, number of kitchen, number of bathrooms, the year the house was

built, and the square footage of the lot. Given these factors, predicting the selling price of

the house is an example of ____________ task.

• Binary Classification

• Multilabel Classification

• Simple Linear Regression

• Multiple Linear Regression

Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model.)

(i). Models which overfit are more likely to have high bias

(ii). Models which overfit are more likely to have low bias

(iii). Models which overfit are more likely to have high variance

(iv). Models which overfit are more likely to have low variance

• (i) and (ii)

• (ii) and (iii)

• (iii) and (iv)

• None of these

State whether the statements are True or False.

Statement A: When the hypothesis space is richer, overfitting is more likely.

Statement B: When the feature space is larger, overfitting is more likely.

• False, False

• True, False

• True, True

• False, True

What is the purpose of restricting hypothesis space in machine learning?

• Can be easier to search

• May avoid overfit since they are usually simpler (e.g. linear or low order decision surface)

• Both of the above

• None of the above

Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?

• You will add more features

• You will start introducing higher degree features

• You will remove some features

• None of the above.

Consider a simple linear regression model with one independent variable (X). The output variable is Y. The equation is : Y=aX+b, where a is the slope and b is the intercept. If we change the input variable (X) by 1 unit, by how much output variable (Y) will change?

• 1 unit

• By slope

• By intercept

• None

You have generated data from a 3-degree polynomial with some noise. What do you expect of the model that was trained on this data using a 5-degree polynomial as function class?

• Low bias, high variance

• High bias, low variance.

• Low bias, low variance.

• High bias, low variance.

What are the optimum number of principal components in the above figure ?

• 10

• 20

• 30

• 40

• a

• b

• c

• d