Implement logistic regression from scratch
Last updated: April 18, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
Rippling
April 18, 202637
5
3,577 solved
Write a clean implementation of logistic regression without using ML libraries.
Machine learning questions at Rippling test both theoretical understanding and practical experience. This Technical 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 detect and handle concept drift?
- 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?
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Explore ML Interview PrepSample Answer
Core Concept: Logistic Regression
Logistic regression is a statistical method for binary classification that models the probability of a binary outcome based on one or more predictor variables. It uses the logistic function to transfo...
How It Works: Mathematical Mechanism
The logistic regression model is fitted to the data using Maximum Likelihood Estimation (MLE), which is a method of estimating the parameters of a statistical model. The likelihood function for logist...
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