Implement k-means from scratch
Last updated: May 21, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
TikTok
May 21, 2026154
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2,979 solved
Write a clean implementation of logistic regression without using ML libraries.
This ML question from TikTok'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 mathematical foundations with clarity
- Discuss practical implementation considerations and hyperparameter tuning
- Analyze the technique's strengths and weaknesses for different data types
- Demonstrate understanding of evaluation methodology and metrics
- Connect theory to real-world applications with concrete examples
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 are the computational costs of this approach at scale?
- When would you prefer a simpler model over a complex one?
- What regularization technique would you use and why?
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Explore ML Interview PrepSample Answer
Core Concept: K-Means Clustering
K-means clustering is an unsupervised learning algorithm that partitions a dataset into distinct clusters based on feature similarity. The objective is to minimize the within-cluster variance,...
How It Works: The Algorithmic Mechanism
The K-means algorithm consists of the following steps:
- Initialization: Randomly select data points as initial centroids.
- Assignment Step: Assign each data point to the nearest ce...
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