Implement linear regression from scratch
Last updated: October 13, 2025
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
DE Shaw
October 13, 20252
10
2,127 solved
Write a clean implementation of logistic regression without using ML libraries.
DE Shaw asks this during the Technical Screen to assess your depth in ML. They expect you to discuss the mathematical foundations, practical considerations, and common pitfalls when applying these techniques in production.
What the Interviewer Expects
- Derive key equations and explain the optimization process in depth
- Discuss state-of-the-art variations and recent research developments
- Analyze computational complexity and scalability
- Implement core components from scratch with clean code
- Discuss production deployment challenges and solutions
- Compare with cutting-edge alternatives and justify your recommendation
Key Topics to Cover
How to Approach This
- Understand the bias-variance trade-off. High training accuracy but low test accuracy signals overfitting.
- Choose evaluation metrics carefully based on the problem. Accuracy alone is often insufficient.
- Feature engineering is often more impactful than model selection.
- Know when to use tree-based models (tabular data) vs neural networks (unstructured data).
- Handle class imbalance with SMOTE, class weights, or appropriate loss functions.
Possible Follow-up Questions
- What regularization technique would you use and why?
- How would you ensure reproducibility in your ML pipeline?
- How would you handle a highly imbalanced dataset?
- How would you explain this model's predictions to a non-technical stakeholder?
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Explore ML Interview PrepSample Answer
Core Concept: Logistic Regression
Logistic regression is a statistical method for predicting binary classes. The core concept hinges on the logistic function, also known as the sigmoid function, which maps any real-valued number into ...
How It Works: Optimization via Gradient Descent
The optimization of the logistic regression model is typically performed using gradient descent. The objective is to minimize the binary cross-entropy loss function, defined as:
[ L(W, b) = -\frac{1...
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