Q. In the context of evaluation metrics, what is a confusion matrix?
A.
A table used to describe the performance of a classification model
B.
A method to visualize the ROC curve
C.
A technique to calculate the AUC
D.
A way to measure the variance in predictions
Solution
A confusion matrix summarizes the performance of a classification algorithm by showing the true positives, false positives, true negatives, and false negatives.
Correct Answer:
A
— A table used to describe the performance of a classification model
Q. In which scenario would you prefer using the Matthews correlation coefficient?
A.
When dealing with binary classification problems
B.
When evaluating multi-class classification problems
C.
When the dataset is highly imbalanced
D.
All of the above
Solution
The Matthews correlation coefficient is a robust metric that can be used for both binary and multi-class classification, especially in imbalanced datasets.
Q. What does a high value of R-squared indicate in regression analysis?
A.
The model explains a large proportion of the variance in the dependent variable
B.
The model has a high number of features
C.
The model is overfitting the training data
D.
The model is underfitting the training data
Solution
A high R-squared value indicates that a significant proportion of the variance in the dependent variable is explained by the independent variables in the model.
Correct Answer:
A
— The model explains a large proportion of the variance in the dependent variable
Q. What does precision indicate in a confusion matrix?
A.
The ratio of true positives to the total predicted positives
B.
The ratio of true positives to the total actual positives
C.
The overall correctness of the model
D.
The ability to identify all relevant instances
Solution
Precision measures the accuracy of positive predictions, calculated as the number of true positives divided by the sum of true positives and false positives.
Correct Answer:
A
— The ratio of true positives to the total predicted positives
Q. What does the AUC represent in the context of the ROC curve?
A.
The area under the curve, indicating the model's ability to distinguish between classes
B.
The average of the true positive rates
C.
The total number of false positives
D.
The accuracy of the model
Solution
AUC (Area Under the Curve) quantifies the overall ability of the model to discriminate between positive and negative classes, with a value of 1 indicating perfect classification.
Correct Answer:
A
— The area under the curve, indicating the model's ability to distinguish between classes
A.
To visualize the trade-off between sensitivity and specificity
B.
To measure the accuracy of a regression model
C.
To determine the optimal threshold for classification
D.
Both A and C
Solution
The ROC curve illustrates the performance of a binary classifier as its discrimination threshold is varied, showing the trade-off between true positive rate and false positive rate.
Q. Which metric is used to evaluate regression models?
A.
F1 Score
B.
Mean Absolute Error
C.
Precision
D.
Recall
Solution
Mean Absolute Error (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction, making it a common metric for regression.
