Implement logistic regression from scratch
Last updated: July 13, 2025
Quick Overview
Write a clean implementation of k-means without using ML libraries.
ServiceNow
July 13, 202585
7
4,564 solved
Write a clean implementation of k-means without using ML libraries.
ServiceNow asks this during the Phone Screen 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
- When would you prefer a simpler model over a complex one?
- How would you ensure reproducibility in your ML pipeline?
- How would you explain this model's predictions to a non-technical stakeholder?
- How would you detect and handle concept drift?
Sharpen Your Skills on Codemia
Practice similar problems with our interactive workspace, get AI feedback, and track your progress.
Explore ML Interview PrepSample Answer
Core Concept: Logistic Regression
Logistic Regression is a statistical method for binary classification that models the relationship between a dependent binary variable and one or more independent variables by using the logistic funct...
How It Works: Mathematical Foundations
The optimization in logistic regression involves maximizing the log-likelihood function:
To find the optimal c...
Submit Your Answer
