Implement linear regression from scratch
Last updated: May 24, 2026
Quick Overview
Write a clean implementation of k-means without using ML libraries.
PlanetScale
May 24, 202625
5
705 solved
Write a clean implementation of k-means without using ML libraries.
This ML question from PlanetScale'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
- 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?
- What regularization technique would you use and why?
- How would you handle a highly imbalanced dataset?
- How would you explain this model's predictions to a non-technical stakeholder?
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Explore ML Interview PrepSample Answer
Core Concept: K-Means Clustering
K-Means clustering is an unsupervised machine learning algorithm used to partition a dataset into distinct groups (clusters) based on feature similarity. The core idea is to minimize the variance with...
How It Works: Mathematical Mechanism
The K-Means algorithm operates in the following steps:
- Initialization: Choose initial centroids randomly from the dataset.
- Assignment Step: For each data point , assign it t...
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