Data Science Analysis 5

Question 1

In a simple linear regression, the R-squared value represents:

Accepted Answer

The proportion of variance in the target explained by the model

Answer

The slope of the regression line

Answer

The number of predictors

Answer

The residual standard error

Question 2

A residual plot showing a clear curved pattern suggests:

Accepted Answer

The linear model may be misspecified

Answer

The model fits perfectly

Answer

There are no outliers

Answer

The data is normally distributed

Question 3

Which metric is most appropriate for evaluating a regression model's prediction error?

Accepted Answer

Root mean squared error (RMSE)

Answer

Accuracy

Answer

F1 score

Answer

Precision

Question 4

On an imbalanced classification dataset, why can accuracy be misleading?

Accepted Answer

A model predicting the majority class can score high while ignoring the minority

Answer

Accuracy cannot be computed

Answer

It always equals precision

Answer

It penalizes the majority class

Question 5

What does precision measure in a classification model?

Accepted Answer

The fraction of predicted positives that are actually positive

Answer

The fraction of actual positives correctly found

Answer

Overall correctness across all classes

Answer

The area under the ROC curve

Question 6

An AUC-ROC value of 0.5 indicates a classifier that:

Accepted Answer

Performs no better than random guessing

Answer

Is perfect

Answer

Always predicts the positive class

Answer

Has zero false positives

Question 7

Overfitting is best described as a model that:

Accepted Answer

Performs well on training data but poorly on unseen data

Answer

Performs poorly on both training and test data

Answer

Has too few parameters

Answer

Ignores the training data entirely