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This is a hard difficulty Machine Learning question, commonly asked during Onsite rounds.

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This question is commonly asked for Data Scientist candidates during technical interviews.

Tune metrics for imbalanced classification | PracHub Interview Question

Quick Overview

This question evaluates a data scientist's competency in machine learning for rare-event detection, testing skills in preprocessing messy data, handling high-cardinality categoricals, designing validation splits, selecting imbalanced evaluation metrics and cost-sensitive decision thresholds, and reasoning about operational trade-offs.

Fraud Detection With Rare Positives (0.5%) and Messy Data

You are designing a supervised transaction-level fraud detector. Positives (fraud) are rare at 0.5% of all cases. The dataset has ~10% missing values, heavy-tailed outliers, and high-cardinality categorical features (e.g., merchant_id, device_id).

Answer the following:

Preprocessing a) Propose a concrete preprocessing pipeline to handle missing values and outliers for both numeric and categorical features. Address high-cardinality categoricals and leakage prevention.
Training/Validation Splits b) Specify how you would split the data for training/validation/testing. Justify stratified cross-validation (and when to prefer time-aware or group-aware schemes).
Evaluation and Costing c) Select and justify evaluation metrics in this imbalanced setting. Compare ROC-AUC vs PR-AUC vs F1 at a chosen threshold. Define a cost-sensitive objective and the optimal decision threshold given costs.
Class Imbalance d) Provide a concrete method to address class imbalance (e.g., calibrated class weights, focal loss, or SMOTE). Explain exactly how to apply it without leakage.
Business Trade-offs e) Give a real-world example where false positives are costlier than false negatives (or vice versa), and explain how that changes thresholding and monitoring in production.

Quick Overview

Fraud Detection With Rare Positives (0.5%) and Messy Data

Answer the following:

Preprocessing a) Propose a concrete preprocessing pipeline to handle missing values and outliers for both numeric and categorical features. Address high-cardinality categoricals and leakage prevention.

Training/Validation Splits b) Specify how you would split the data for training/validation/testing. Justify stratified cross-validation (and when to prefer time-aware or group-aware schemes).

Evaluation and Costing c) Select and justify evaluation metrics in this imbalanced setting. Compare ROC-AUC vs PR-AUC vs F1 at a chosen threshold. Define a cost-sensitive objective and the optimal decision threshold given costs.

Class Imbalance d) Provide a concrete method to address class imbalance (e.g., calibrated class weights, focal loss, or SMOTE). Explain exactly how to apply it without leakage.

Business Trade-offs e) Give a real-world example where false positives are costlier than false negatives (or vice versa), and explain how that changes thresholding and monitoring in production.

Tune metrics for imbalanced classification

Quick Overview

Tune metrics for imbalanced classification

Fraud Detection With Rare Positives (0.5%) and Messy Data

Write your answer

Tune metrics for imbalanced classification

Quick Overview

Tune metrics for imbalanced classification

Fraud Detection With Rare Positives (0.5%) and Messy Data

Write your answer