Implement logistic regression from scratch
Last updated: May 12, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
Uber
May 12, 20266
7
803 solved
Write a clean implementation of logistic regression without using ML libraries.
Uber asks this during the Take-home Project 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
- 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
- When would you prefer a simpler model over a complex one?
- How would you handle a highly imbalanced dataset?
- How would you ensure reproducibility in your ML pipeline?
- 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. The core concept revolves around estimating the probability that a given input belongs to a certain class (e.g., whether a r...
How It Works: The Mathematical Mechanism
The logistic regression model is trained using a method called Maximum Likelihood Estimation (MLE). The goal is to find the parameter values that maximize the likelihood of the observed da...
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