CompTIA Data+ (DA0-002)Data AnalysisMedium
A data scientist is developing a machine learning model to classify customer reviews as positive or negative. After training the model, they evaluate its performance using a test dataset. The model correctly identifies 90% of all positive reviews and 85% of all negative reviews. However, when examining the reviews predicted as positive, only 70% were actually positive, while 30% were incorrectly classified. Which metric is described by the 70% figure?
- AF1-Score
- BRecall
- CPrecision
- DAccuracy
Show answer & explanationAnswer & explanation
Correct answer: C. Precision
Precision measures the proportion of positive identifications that were actually correct. In this scenario, out of all reviews the model predicted as positive, 70% were truly positive, which aligns with the definition of precision.
Why the other options are wrong
- A. F1-Score is the harmonic mean of precision and recall, providing a balanced measure.
- B. Recall (Sensitivity) measures the proportion of actual positives that were correctly identified (90% of all positive reviews).
- D. Accuracy measures the overall proportion of correct predictions (both positive and negative) out of the total predictions.
Precision (Classification)
In classification, precision is the ratio of true positive predictions to the total number of positive predictions (True Positives + False Positives). It answers: 'Of all items predicted as positive, how many were actually positive?'
- Focuses on the correctness of positive predictions.
- Calculated as True Positives / (True Positives + False Positives).
- High precision means fewer false positive errors.
Memory trick: PR-ecision: P-ositive R-eports are Correct.