Boston Consulting Group Machine Learning Interview Questions
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Design and sample for credit default prediction
A bank wants a model to predict 90-day credit card default at account-month level for proactive outreach. Class prevalence in production is about 2% d...
Explain AUC, imbalance, losses, and networks
Imbalanced Classification & Regression: ROC/PR, Losses, and Training Strategies You are evaluating a binary classifier and a regression head in a mach...
Build and evaluate imbalanced binary classifier
Take‑home: Imbalanced Binary Classification with Temporal Split, Calibration, and Operating Point Selection Context You are given an event‑level datas...
Reduce overfitting under constraints
Reduce Overfitting Under Latency Constraints (Tabular Regression) Context (assumed) - You have a tabular regression model with a large generalization ...
Achieve 0.95 precision via thresholding
Deploying a High-Precision Classifier on an Imbalanced Dataset You are given a binary classification problem with 50,000 samples and ~5% positives. Th...
Build a leak-free sklearn pipeline
Take-home: Imbalanced Binary Classification Pipeline with scikit-learn You are training a binary classifier on tabular data with the following feature...
Explain AUC, activations, ensembles, and imbalance
Machine Learning Metrics and Modeling Choices — Multi-part You are given model scores and binary labels for a small dataset and asked to compute ROC A...
Detect Data Leakage in Supervised Learning Pipelines
ML Take‑home: Bias–Variance, Regularization, Leakage, and From‑scratch Logistic Regression Context You are given user event logs in a Pandas dataframe...
Interpret AUC Values and Handle Class Imbalance Techniques
AUC and Class Imbalance in Binary Classification Context You are evaluating a binary classifier using ROC–AUC and need to reason about performance und...
Improve Model Generalization with Cross-Validation and Feature Engineering
Predict Next-Month Orders: Train/Test Split, Pipeline, and AUC Context You are given a cleaned tabular retail dataset as a pandas DataFrame df. The bi...
Differentiate Overfitting and Underfitting in Machine Learning
ML/DL Fundamentals for a Recommendation Engine Context You are preparing for a take-home assessment on ML/DL fundamentals relevant to building a recom...
Train GradientBoostingClassifier with 5-Fold Cross-Validation
Final Model Training: GradientBoostingClassifier with 5-Fold CV Context Assume the notebook already contains a prepared feature matrix X and a binary ...
Scale and Normalize: When to Use Each Method?
Feature Scaling Before Modeling (CodeSignal Notebook) Context You're preparing features in a notebook step before training a model. You have a pandas ...