✅ Data Science Interview Questions with Answers Part-9

81. What is accuracy and when is it misleading?
Accuracy measures the proportion of correct predictions out of total predictions. It becomes misleading when classes are imbalanced because a model can predict the majority class and still achieve high accuracy while performing poorly on the minority class.

82. What is precision and recall?
- Precision: How many predicted positive cases are actually positive.
- Recall: How many actual positive cases are correctly identified.
Precision focuses on false positives, while recall focuses on false negatives.

83. What is F1 score?
F1 score is the harmonic mean of precision and recall. It provides a balanced measure when both false positives and false negatives matter, especially in imbalanced datasets.

84. What is ROC curve?
The ROC curve plots the true positive rate against the false positive rate at different threshold values. It shows how well a model distinguishes between classes across thresholds.

85. What is AUC?
Area Under the ROC Curve measures overall model performance. A higher AUC indicates better ability to separate classes regardless of threshold choice.

86. Difference between confusion matrix metrics?
A confusion matrix breaks predictions into true positives, true negatives, false positives, and false negatives. Metrics like accuracy, precision, recall, and F1 are derived from these values to evaluate performance.

87. What is log loss?
Log loss measures the performance of a classification model by penalizing incorrect and overconfident predictions. Lower log loss indicates better probability estimates.

88. What is RMSE?
Root Mean Squared Error measures the average magnitude of prediction errors in regression tasks. It penalizes large errors more heavily than small ones and is sensitive to outliers.

89. What metric do you use for imbalanced data?
For imbalanced data, metrics such as precision, recall, F1 score, ROC-AUC, or PR-AUC are used instead of accuracy. The choice depends on business cost of errors.

90. How do business metrics link to ML metrics?
ML metrics must align with business goals. For example, recall may map to fraud prevention, while precision may map to cost control. The model is successful only if improvements in ML metrics lead to measurable business impact.

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