MS-DS Master of Data science Supervised Learning Algorithms 2

Question 1

In a support vector machine, what is the role of the kernel trick?

Accepted Answer

It maps data into a higher-dimensional space to find a linear separator

Answer

The kernel trick implicitly maps input features to a higher-dimensional space where a linear decision boundary can separate classes that are non-linearly separable in the original space.

Question 2

Which loss function is most appropriate for a multi-class classification problem with mutually exclusive classes?

Accepted Answer

Categorical cross-entropy

Answer

Categorical cross-entropy measures the difference between the predicted probability distribution and the one-hot encoded true class label across all mutually exclusive classes.

Question 3

What does the 'max_depth' hyperparameter control in a decision tree?

Accepted Answer

The longest path from the root to a leaf node

Answer

max_depth limits the longest path from the root to any leaf, controlling model complexity and preventing overfitting by restricting how deep the tree can grow.

Question 4

A logistic regression model outputs a probability of 0.3 for the positive class. With a threshold of 0.5, how is this classified?

Accepted Answer

Negative class, because 0.3 < 0.5

Answer

With a decision threshold of 0.5, any predicted probability below 0.5 is assigned to the negative class, so 0.3 maps to the negative class.

Question 5

Which of the following best describes the bias-variance tradeoff in supervised learning?

Accepted Answer

High-bias models underfit the training data; high-variance models overfit it

Answer

High bias means the model is too simple and underfits, while high variance means the model is too complex and overfits, capturing noise in the training data.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Supervised Learning Algorithms 2