Implement naive bayes from scratch
Last updated: August 25, 2025
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
Instacart
August 25, 202587
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Write a clean implementation of logistic regression without using ML libraries.
This ML question from Instacart'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 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
- When would you prefer a simpler model over a complex one?
- How would you explain this model's predictions to a non-technical stakeholder?
- What regularization technique would you use and why?
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Explore ML Interview PrepSample Answer
Core Concept: Logistic Regression
Logistic regression is a supervised learning algorithm used for binary classification problems. Unlike linear regression, which predicts continuous outcomes, logistic regression predicts the probabili...
How It Works: Gradient Descent Optimization
To implement logistic regression from scratch, we typically use gradient descent to optimize the coefficients . The cost function used is the binary cross-entropy loss:
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