Implement linear regression from scratch
Last updated: February 5, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
Postmates
February 5, 202613
6
4,556 solved
Write a clean implementation of logistic regression without using ML libraries.
Machine learning questions at Postmates test both theoretical understanding and practical experience. This Phone Screen question evaluates your knowledge of ML fundamentals and your ability to apply them to real-world problems.
What the Interviewer Expects
- Explain the concept clearly with intuitive examples
- Discuss when and why to use this technique
- Identify common pitfalls and how to avoid them
- Compare with alternative approaches at a high level
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?
- When would you prefer a simpler model over a complex one?
- How would you explain this model's predictions to a non-technical stakeholder?
- How would you detect and handle concept drift?
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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 a continuous outcome, logistic regression predicts the probability that a ...
How It Works: Mathematical Mechanism
The logistic regression model estimates the coefficients using the Maximum Likelihood Estimation (MLE). The likelihood function is given by:
[ L(\beta) = \prod_{i=1}^{m} p(y_i | x_i; \be...
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