Implement linear regression from scratch
Last updated: February 6, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
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Write a clean implementation of logistic regression without using ML libraries.
Google asks this during the Onsite 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
- How would you handle a highly imbalanced dataset?
- What regularization technique would you use and why?
- What are the computational costs of this approach at scale?
- When would you prefer a simpler model over a complex one?
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Explore ML Interview PrepSample Answer
Core Concept: Logistic Regression
Logistic regression is a statistical method used for binary classification problems. Unlike linear regression which predicts continuous outcomes, logistic regression predicts the probability that a gi...
How It Works: Mathematical Derivation and Optimization
The optimization of the logistic regression model is typically approached using maximum likelihood estimation (MLE). The likelihood function for logistic regression is:
[ L(\beta) = \prod_{i=1}^{m} ...
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