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Machine-learning Questions Set 3:

Quiz Mode

What is the MNIST dataset used for?

Handwriting Text Recognition

Emotion Detection

Recommendation System

Cancer Diagnosis System

Solution:

In which of the following cases does machine learning come into the picture?

Buying vegetables

Movie recommendation system

Ordering coffee in a cafe

Baking a cake

Solution:

Adding a non-important feature to a linear regression model will result in:

Increase in R-squared

Decrease in R-squared

No change in R-squared

Unpredictable change in R-squared

Solution:

Which of the following hyperparameters, when increased, may cause a random forest model to overfit the data?

Number of trees

Depth of trees

Number of trees and depth of trees

Number of trees and learning rate

Solution:

In a situation with multicollinear features, which of the following actions would you perform next?

Remove both collinear variables

Remove only one of the collinear variables

Remove one variable and use penalized regression models

Either option 2 or option 3

Solution:

Which of the following statements about Type-1 and Type-2 errors is/are true?

Type 1 is known as false positive and Type 2 is known as false negative.

Type 1 is known as false negative and Type 2 is known as false positive.

Only 1

Type 1 error occurs when we reject a null hypothesis when it is actually true.

Solution:

What are the three basic Machine Learning Paradigms?

Supervised Learning, Computer Vision, Unsupervised Learning

Supervised Learning, Unsupervised Learning, Reinforcement Learning

Image Recognition, Supervised Learning, Natural Language Processing

Image Recognition, Unsupervised Learning, Natural Language Processing

Solution:

Suppose we have a dataset that can be trained with 100% accuracy using a decision tree of depth 6. Now, consider the following points and choose the option that best describes the characteristics of a decision tree with depth 4:

Depth 4 will have high bias and low variance.

Depth 4 will have low bias and low variance.

Both 1 and 2

None of the above

Solution:

What is Machine Learning?

At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

All of the above

Machine learning is the study of computer algorithms that improve automatically through experience

Solution:

Suppose you want to project high dimensional data into lower dimensions. The two most famous dimensionality reduction algorithms used here are Principal Component Analysis (PCA) and t-SNE. Let's say you have applied both algorithms respectively on data 'X' and you got the datasets 'X_projected_PCA' and 'X_projected_tSNE'. Which of the following statements is true for 'X_projected_PCA' and 'X_projected_tSNE'?

X_projected_PCA will have interpretation in the nearest neighbor space.

X_projected_tSNE will have interpretation in the nearest neighbor space.

Both will have interpretation in the nearest neighbor space.

None of them will have interpretation in the nearest neighbor space.

Solution:

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Machine-learning Questions Set 3:

What is the MNIST dataset used for?

Solution:

In which of the following cases does machine learning come into the picture?

Solution:

Adding a non-important feature to a linear regression model will result in:

Solution:

Which of the following hyperparameters, when increased, may cause a random forest model to overfit the data?

Solution:

In a situation with multicollinear features, which of the following actions would you perform next?

Solution:

Which of the following statements about Type-1 and Type-2 errors is/are true?

Solution:

What are the three basic Machine Learning Paradigms?

Solution:

Suppose we have a dataset that can be trained with 100% accuracy using a decision tree of depth 6. Now, consider the following points and choose the option that best describes the characteristics of a decision tree with depth 4:

Solution:

What is Machine Learning?

Solution:

Solution:

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