Implement k-means from scratch
Last updated: July 13, 2025
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
SentinelOne
July 13, 202543
6
3,079 solved
Write a clean implementation of logistic regression without using ML libraries.
SentinelOne asks this during the Onsite 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 detect and handle concept drift?
- How would you explain this model's predictions to a non-technical stakeholder?
- What regularization technique would you use and why?
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Explore ML Interview PrepSample Answer
Core Concept: K-Means Clustering
K-means is an unsupervised learning algorithm used for clustering data into distinct groups based on feature similarity. The primary goal of K-means is to partition n observations into k clusters in w...
How It Works: Algorithm and Optimization
K-means operates through a two-step iterative process:
- Assignment Step: Assign each data point to the nearest centroid based on the Euclidean distance. This can be formalized as:
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