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This is a hard difficulty Machine Learning question, commonly asked during Take-home Project rounds at DRW.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at DRW during technical interviews.

Build an imbalanced classification pipeline with sklearn

Quick Overview

This question evaluates a candidate's competency in building an end-to-end imbalanced binary classification pipeline, covering preprocessing, resampling strategies, classifier comparison, cross-validated hyperparameter tuning, and evaluation metrics (ROC-AUC, PR-AUC) with tools like scikit-learn and imbalanced-learn.

Take-home: End-to-end Imbalanced Binary Classification Pipeline (scikit-learn + imbalanced-learn)

Context

You are given a tabular, imbalanced binary classification problem (y ∈ {0, 1}, with minority class 1). Build a clean, reproducible pipeline that prevents data leakage, compares imbalance strategies, and delivers evaluation artifacts and threshold tuning.

Requirements

Data preprocessing
- Use a Pipeline (and a ColumnTransformer if there are mixed numeric/categorical features) to perform:
  - Numeric scaling
  - Categorical encoding
  - Class imbalance handling via resampling
  - Modeling
- Ensure the resampling step happens inside each cross-validation fold to avoid leakage.
Imbalance strategies to compare
- Class-weight adjustments (e.g., class_weight="balanced") without explicit resampling.
- Explicit resampling (e.g., SMOTE or RandomUnderSampler) with class_weight=None.
Modeling and tuning
- Try at least two classifiers (e.g., LogisticRegression and RandomForest). You may optionally include an XGBoost-compatible estimator.
- Hyperparameter tuning with StratifiedKFold cross-validation.
- Primary metric: ROC-AUC. Also report PR-AUC (Average Precision).
- Use GridSearchCV (or equivalent) with scoring={'roc_auc', 'average_precision'} and refit='roc_auc'.
Evaluation artifacts and thresholding
- On a held-out test set, produce:
  - Confusion matrix at a selected threshold
  - ROC curve and PR curve
  - Calibration curve
- Demonstrate threshold tuning to:
  - Maximize F1, and
  - Maximize recall subject to precision ≥ 0.90.
Reproducibility and documentation
- Fixed random seeds; clean, well-documented code.
- Clear notes on how leakage is prevented and how to adapt to a real dataset.

Quick Overview

Requirements

Data preprocessing

Use a Pipeline (and a ColumnTransformer if there are mixed numeric/categorical features) to perform:
- Numeric scaling
- Categorical encoding
- Class imbalance handling via resampling
- Modeling
Ensure the resampling step happens inside each cross-validation fold to avoid leakage.

Imbalance strategies to compare

Class-weight adjustments (e.g., class_weight="balanced") without explicit resampling.
Explicit resampling (e.g., SMOTE or RandomUnderSampler) with class_weight=None.

Modeling and tuning

Try at least two classifiers (e.g., LogisticRegression and RandomForest). You may optionally include an XGBoost-compatible estimator.
Hyperparameter tuning with StratifiedKFold cross-validation.
Primary metric: ROC-AUC. Also report PR-AUC (Average Precision).
Use GridSearchCV (or equivalent) with scoring={'roc_auc', 'average_precision'} and refit='roc_auc'.

Evaluation artifacts and thresholding

On a held-out test set, produce:
- Confusion matrix at a selected threshold
- ROC curve and PR curve
- Calibration curve
Demonstrate threshold tuning to:
- Maximize F1, and
- Maximize recall subject to precision ≥ 0.90.

Reproducibility and documentation

Fixed random seeds; clean, well-documented code.
Clear notes on how leakage is prevented and how to adapt to a real dataset.

Build an imbalanced classification pipeline with sklearn

Quick Overview

Take-home: End-to-end Imbalanced Binary Classification Pipeline (scikit-learn + imbalanced-learn)

Context

Requirements

Solution

Comments (0)

Build an imbalanced classification pipeline with sklearn

Quick Overview

Take-home: End-to-end Imbalanced Binary Classification Pipeline (scikit-learn + imbalanced-learn)

Context

Requirements

Solution

Comments (0)