Implement logistic regression from scratch
Last updated: December 18, 2025
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
Supabase
December 18, 20250
9
650 solved
Write a clean implementation of logistic regression without using ML libraries.
This ML question from Supabase's Technical Screen goes beyond textbook definitions. The interviewer wants to see how you reason about model selection, evaluation metrics, and the practical challenges of deploying ML in production.
What the Interviewer Expects
- Explain the mathematical foundations with clarity
- Discuss practical implementation considerations and hyperparameter tuning
- Analyze the technique's strengths and weaknesses for different data types
- Demonstrate understanding of evaluation methodology and metrics
- Connect theory to real-world applications with concrete examples
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?
- What are the computational costs of this approach at scale?
- How would you explain this model's predictions to a non-technical stakeholder?
- How would you handle a highly imbalanced dataset?
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Explore ML Interview PrepSample Answer
Core Concept: Logistic Regression
Logistic regression is a statistical model used for binary classification that predicts the probability of an outcome based on one or more predictor variables. Unlike linear regression, which outputs ...
How It Works: Optimization and Loss Function
In logistic regression, the model parameters are estimated by maximizing the likelihood function or, equivalently, minimizing the binary cross-entropy loss. The loss function is defined as...
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