✅ *AI Model Evaluation Interview Questions & Answers* 🧠📊
*1️⃣ Q: What is a confusion matrix, and how do you interpret it?*
*A:* A confusion matrix is a performance measurement tool for classification problems. It displays the counts of:
• *True Positives (TP):* Correctly predicted positive cases
• *True Negatives (TN):* Correctly predicted negative cases
• *False Positives (FP):* Incorrectly predicted positives (Type I error)
• *False Negatives (FN):* Incorrectly predicted negatives (Type II error)
It helps identify where the model is making mistakes.
*2️⃣ Q: How do you calculate accuracy, and when is it a bad metric?*
*A:*
*Accuracy = (TP + TN) / (TP + TN + FP + FN)*
It shows how often the classifier is correct.
*When it's bad:* In *imbalanced datasets* (e.g., 95% negative), a model can achieve high accuracy by always predicting the majority class, while missing minority class predictions completely.
*3️⃣ Q: What is the difference between precision and recall?*
*A:*
• *Precision = TP / (TP + FP)* → Out of predicted positives, how many were correct?
• *Recall = TP / (TP + FN)* → Out of actual positives, how many did we correctly predict?
*Use precision* when *false positives are costly* (e.g., spam detection).
*Use recall* when *false negatives are costly* (e.g., cancer detection).
*4️⃣ Q: What is the F1 Score and why is it important?*
*A:*
F1 Score is the *harmonic mean of precision and recall*.
*F1 = 2 * (Precision * Recall) / (Precision + Recall)*
It balances precision and recall, and is especially useful when the dataset is *imbalanced*.
*5️⃣ Q: Explain the ROC curve and AUC.*
*A:*
The ROC (Receiver Operating Characteristic) curve plots:
• *True Positive Rate (Recall)* vs. *False Positive Rate* at different thresholds.
*AUC (Area Under Curve)* represents the model’s ability to distinguish between classes.
• AUC = 1 → Perfect model
• AUC = 0.5 → Random model
Useful for comparing models regardless of threshold.
*6️⃣ Q: When would you prefer a Precision-Recall (PR) curve over ROC?*
*A:*
Use a PR curve when dealing with *highly imbalanced datasets*.
ROC can give misleadingly optimistic results because it includes *true negatives*, which dominate in imbalance. PR focuses on the positive class performance.
*7️⃣ Q: What is cross-validation and why is it used?*
*A:*
Cross-validation helps assess a model’s generalizability.
*k-Fold Cross-Validation:*
• Split data into k parts
• Train on k-1 parts, test on the remaining
• Repeat k times and average the results
It reduces the risk of overfitting and gives a better estimate of performance on unseen data.
*8️⃣ Q: What is the bias-variance tradeoff in model evaluation?*
*A:*
• *High bias:* Model is too simple → underfitting
• *High variance:* Model is too complex → overfitting
You want a balance between the two:
• Low training error + low gap between training/test error = ideal
*9️⃣ Q: How do you evaluate model performance on an imbalanced dataset?*
*A:*
• Use *Precision*, *Recall*, *F1-Score*, *PR Curve*
• Avoid relying only on accuracy
• Consider resampling methods (e.g., *SMOTE*)
• Use *class weighting* or *cost-sensitive learning*
These techniques ensure minority class performance is properly measured.
*🔟 Q: What is log loss, and when is it used?*
*A:*
Log Loss (Logarithmic Loss) evaluates a classification model's probability estimates.
*Lower log loss = better calibrated probabilities.*
Used in scenarios where *probability confidence* matters (e.g., churn prediction).
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