Implement linear regression from scratch
Last updated: January 24, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
Zillow
January 24, 202653
7
803 solved
Write a clean implementation of logistic regression without using ML libraries.
This ML question from Zillow'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
- 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
- How would you ensure reproducibility in your ML pipeline?
- What regularization technique would you use and why?
- How would you handle a highly imbalanced dataset?
- What are the computational costs of this approach at scale?
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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 binary outcome based on one or more predictor variables. Unlike linear regression, which pre...
How It Works: Mathematical Foundation
In logistic regression, the model is trained by maximizing the likelihood function, which measures how well the model predicts the observed data. The cost function is the negative log-likelihood: ( J...
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