Machine Learning Regression 2

Question 1

Which metric measures the average of squared differences between predicted and actual values?

Accepted Answer

Mean Squared Error

Answer

Mean Squared Error (MSE) computes the average of squared residuals, penalizing large errors more heavily than MAE.

Question 2

In Ridge regression, what happens to the coefficients as the regularization parameter λ increases?

Accepted Answer

Coefficients shrink toward zero

Answer

Ridge regression adds an L2 penalty that shrinks all coefficients toward (but not exactly to) zero as λ increases.

Question 3

A regression model has R² = 0.85. What does this indicate?

Accepted Answer

The model explains 85% of the variance in the target variable

Answer

R² (coefficient of determination) represents the proportion of variance in the dependent variable explained by the model.

Question 4

Which assumption of linear regression states that residuals should not be correlated with each other?

Accepted Answer

Independence of errors

Answer

The independence of errors assumption requires that residuals are uncorrelated — violation often occurs in time-series data.

Question 5

What does the Durbin-Watson statistic test for in regression analysis?

Accepted Answer

Autocorrelation in residuals

Answer

The Durbin-Watson statistic detects autocorrelation in residuals; values near 2 indicate no autocorrelation.

Machine Learning Practice Test