Explain the Confusion Matrix and its components.
Direct Answer
Candidates must describe the structure of a confusion matrix and define True/False Positives/Negatives.
Why Interviewers Ask This
The confusion matrix is the foundation for calculating most classification metrics. Interviewers ask this to ensure you have a solid grasp of the basic terminology required to discuss model performance. Without this understanding, subsequent questions about precision, recall, or accuracy become difficult to answer accurately.
How to Answer This Question
Describe the confusion matrix as a table comparing predicted labels against actual labels. Break down the four quadrants: True Positives (correctly predicted positive), True Negatives (correctly predicted negative), False Positives (Type I error), and False Negatives (Type II error). Explain how these four numbers serve as the basis for calculating Accuracy, Precision, Recall, and F1-Score.
Key Points to Cover
- It compares predicted vs actual class labels.
- TP and TN represent correct predictions.
- FP and FN represent incorrect predictions.
- It is the basis for calculating precision, recall, and accuracy.
Sample Answer
A confusion matrix is a table used to evaluate the performance of a classification model by comparing predicted labels with actual labels. It consists of four key components: True Positives (TP) where the model correctly…
Common Mistakes to Avoid
- Mixing up False Positive and False Negative definitions.
- Forgetting to mention it applies to classification.
- Not explaining how it relates to other metrics.
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