Implement linear regression from scratch
Last updated: October 23, 2025
Quick Overview
Write a clean implementation of k-means without using ML libraries.
Notion
October 23, 20258
10
242 solved
Write a clean implementation of k-means without using ML libraries.
This ML question from Notion's Phone 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 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
- What regularization technique would you use and why?
- How would you handle a highly imbalanced dataset?
- How would you detect and handle concept drift?
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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 core idea is to partition the data into clusters, where each data poi...
How It Works: Algorithmic Mechanism
The K-means algorithm follows a series of iterative steps:
- Initialization: Select initial centroids randomly from the dataset.
- Assignment Step: Assign each data point to the neares...
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