Implement logistic regression from scratch
Last updated: April 30, 2026
Quick Overview
Write a clean implementation of k-means without using ML libraries.
Shopify
April 30, 20260
6
4,458 solved
Write a clean implementation of k-means without using ML libraries.
Machine learning questions at Shopify test both theoretical understanding and practical experience. This Phone Screen question evaluates your knowledge of ML fundamentals and your ability to apply them to real-world problems.
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
- How would you handle a highly imbalanced dataset?
- How would you ensure reproducibility in your ML pipeline?
- How would you detect and handle concept drift?
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Explore ML Interview PrepSample Answer
Core Concept: Logistic Regression
Logistic regression is a statistical method used for binary classification that models the probability of a class label using the logistic function. The core concept is to predict the probability that...
How it Works: Optimization and Cost Function
In logistic regression, the optimization of the model parameters is performed by minimizing the cost function, which is the log-loss function. The log-loss function is defined as:
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