Metrics

Question 1

What does R² (Coefficient of Determination) measure in regression?

Answer 1

R² measures how well the predicted values explain the variance in the actual values, indicating the model’s fit.

Question 2

What is Mean Absolute Error (MAE) used for in regression?

Answer 2

MAE measures the average absolute difference between predicted and actual values, indicating prediction accuracy.

Question 3

What is Mean Squared Error (MSE), and why is it used?

Answer 3

MSE calculates the average squared differences between predicted and actual values, penalizing larger errors more heavily.

Question 4

What does Root Mean Squared Error (RMSE) represent in regression?

Answer 4

RMSE is the square root of MSE, providing error measurements in the same units as the target variable.

Question 5

What is Adjusted R², and how does it differ from R²?

Answer 5

Adjusted R² accounts for the number of predictors in the model, preventing overestimation of model performance.

Question 6

What does Accuracy measure in classification tasks?

Answer 6

Accuracy is the proportion of correctly classified instances out of the total instances.

Question 7

What is Precision in classification metrics?

Answer 7

Precision is the ratio of true positive predictions to all positive predictions, reflecting model specificity.

Question 8

What does Recall indicate in classification?

Answer 8

Recall, or sensitivity, is the ratio of true positive predictions to all actual positives, measuring model completeness and sensitivity.

Question 9

What is the F1-Score used for in classification?

Answer 9

The F1-Score is the harmonic mean of Precision and Recall, balancing the two in cases of class imbalance.

Question 10

What does the Area Under ROC Curve (AUC) signify?

Answer 10

AUC measures a classifier’s ability to distinguish between classes across various thresholds, with 1 being perfect.

Question 11

What is the Area Under Precision-Recall Curve (PR AUC)?

Answer 11

PR AUC evaluates classifier performance for imbalanced datasets by focusing on Precision and Recall trade-offs.

Question 12

What information does a Confusion Matrix provide?

Answer 12

A Confusion Matrix summarizes classification predictions, showing counts of true positives, false positives, false negatives, and true negatives.

Question 13

What is Mean Average Precision (MAP) used for in ranking tasks?

Answer 13

MAP evaluates the precision at multiple ranks, averaged across all queries, for ranking relevance.

Question 14

What does Mean Reciprocal Rank (MRR) measure?

Answer 14

MRR calculates the average of reciprocal ranks for the first relevant item in ranked results, indicating query response quality.

Question 15

What is the Silhouette Score in clustering?

Answer 15

The Silhouette Score measures how well data points fit within their clusters compared to other clusters, ranging from -1 to 1.

Question 16

What is the purpose of Macro-Averaged Precision in multi-class classification?

Answer 16

Macro-Averaged Precision computes the unweighted average Precision across all classes, treating each class equally.

Question 17

What is Micro-Averaged Recall used for in multi-class classification?

Answer 17

Micro-Averaged Recall aggregates true positives and false negatives across the whole dataset. Gives equal weight to each instance.

Question 18

What does Macro-Averaged F1-Score indicate in multi-class metrics?

Answer 18

Macro-Averaged F1-Score calculates the F1-Score for each class and averages them, providing a balanced view of performance.

Question 19

What does a Multi-Class Confusion Matrix show?

Answer 19

A Multi-Class Confusion Matrix shows prediction counts for all possible class pairings, highlighting misclassification patterns.