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Quiz 5: Evaluation Metrics
Test your understanding of Performance Metrics and their use in evaluating machine learning models.
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1. What does the Confusion Matrix help evaluate?
The relationship between features in a dataset
The performance of a classification model
The variance of a regression model
The distribution of training data
2. Which metric measures the proportion of correctly classified instances out of the total instances?
Precision
Recall
Accuracy
F1-Score
3. What does Precision quantify?
The proportion of actual positives correctly identified
The proportion of true positive predictions out of all positive predictions
The harmonic mean of Precision and Recall
The number of false negatives in the model
4. What is the formula for Recall?
TP / (TP + FP)
TP / (TP + FN)
FP / (TP + FP)
TP + TN / Total
5. What does the F1-Score represent?
The harmonic mean of Precision and Recall
The sum of True Positives and True Negatives
The difference between Accuracy and Precision
The average of False Positives and False Negatives
6. In a Confusion Matrix, what does a False Positive (FP) represent?
A negative case predicted as negative
A positive case predicted as positive
A negative case predicted as positive
A positive case predicted as negative
7. Why is balancing Precision and Recall important in model evaluation?
To maximize the number of true negatives
To improve the variance of the model
To ensure optimal performance in context-specific tasks
To minimize training time
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