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

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This question is commonly asked for Data Scientist candidates at Boston Consulting Group during technical interviews.

Detect Data Leakage in Supervised Learning Pipelines | Boston Consulting Group Interview Question

Quick Overview

This question evaluates a candidate's competency in detecting and preventing data leakage in supervised learning pipelines, understanding bias–variance decomposition, recognizing regularization effects on sparsity, and implementing a temporally correct logistic regression with held‑out AUC evaluation.

ML Take‑home: Bias–Variance, Regularization, Leakage, and From‑scratch Logistic Regression

Context

You are given user event logs in a Pandas dataframe df with columns:

user_id: unique user identifier
event_time: timestamp of the event
event_type: categorical event name (e.g., view, click, add_to_cart, purchase)
purchase: indicator (0/1) if the event is a purchase

Your goal is to build a leakage‑free binary classifier that predicts whether a user will purchase within the next 7 days, then evaluate AUC on a held‑out set.

Tasks

Bias–variance decomposition
- State the bias and variance terms and interpret them in the bias–variance decomposition.
Regularization and sparsity
- Which regularization technique(s) can shrink linear‑model coefficients exactly to zero, and why?
Detecting data leakage
- Name two practical approaches for detecting data leakage in a supervised learning pipeline.
Modeling: Logistic regression from scratch
- Using df(user_id, event_time, event_type, purchase), build a binary classifier to predict whether a user will purchase within the next 7 days.
- Use a temporally correct split and report AUC on a held‑out set.
- Implement logistic regression with gradient descent using only numpy for the model (pandas allowed for data prep). Provide basic convergence diagnostics.

Implementation requirements

Ensure no temporal leakage: features must use data up to an anchor time; labels look forward 7 days after the anchor.
Clean, vectorized Python; no sklearn for the model or metrics (implement AUC yourself).

Quick Overview

Context

You are given user event logs in a Pandas dataframe df with columns:

user_id: unique user identifier

event_time: timestamp of the event

event_type: categorical event name (e.g., view, click, add_to_cart, purchase)

purchase: indicator (0/1) if the event is a purchase

Your goal is to build a leakage‑free binary classifier that predicts whether a user will purchase within the next 7 days, then evaluate AUC on a held‑out set.

Tasks

Bias–variance decomposition

State the bias and variance terms and interpret them in the bias–variance decomposition.

Regularization and sparsity

Which regularization technique(s) can shrink linear‑model coefficients exactly to zero, and why?

Detecting data leakage

Name two practical approaches for detecting data leakage in a supervised learning pipeline.

Modeling: Logistic regression from scratch

Using df(user_id, event_time, event_type, purchase), build a binary classifier to predict whether a user will purchase within the next 7 days.
Use a temporally correct split and report AUC on a held‑out set.
Implement logistic regression with gradient descent using only numpy for the model (pandas allowed for data prep). Provide basic convergence diagnostics.

Detect Data Leakage in Supervised Learning Pipelines

Quick Overview

ML Take‑home: Bias–Variance, Regularization, Leakage, and From‑scratch Logistic Regression

Context

Tasks

Implementation requirements

Solution

Comments (0)

Detect Data Leakage in Supervised Learning Pipelines

Quick Overview

ML Take‑home: Bias–Variance, Regularization, Leakage, and From‑scratch Logistic Regression

Context

Tasks

Implementation requirements

Solution

Comments (0)