DSE Data Science 4

Question 1

Which of the following is a key assumption of linear regression?

Accepted Answer

Residuals are normally distributed and homoscedastic

Answer

Linear regression assumes that residuals have constant variance (homoscedasticity) and are normally distributed.

Question 2

What problem does the SMOTE technique address in machine learning?

Accepted Answer

Class imbalance in the training dataset

Answer

SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class to balance the dataset.

Question 3

In the context of unsupervised learning, what does the silhouette score measure?

Accepted Answer

How well each data point fits its own cluster versus neighboring clusters

Answer

The silhouette score ranges from -1 to 1, where higher values indicate well-separated, cohesive clusters.

Question 4

What is the primary advantage of using gradient boosting over random forests?

Accepted Answer

It sequentially corrects errors from previous trees, often achieving lower bias

Answer

Gradient boosting fits each new tree to the residuals of the ensemble so far, reducing bias iteratively.

Question 5

Which metric is best suited for evaluating a regression model's performance?

Accepted Answer

Root Mean Squared Error (RMSE)

Answer

RMSE measures the average magnitude of prediction errors in the same units as the target variable.

DSE - Data Science Practice Test