Implement linear regression from scratch
Last updated: May 23, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
MongoDB
May 23, 202637
8
3,588 solved
Write a clean implementation of logistic regression without using ML libraries.
Machine learning questions at MongoDB test both theoretical understanding and practical experience. This Onsite question evaluates your knowledge of ML fundamentals and your ability to apply them to real-world problems.
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
- How would you detect and handle concept drift?
- When would you prefer a simpler model over a complex one?
- How would you ensure reproducibility in your ML pipeline?
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Explore ML Interview PrepSample Answer
Core Concept: Logistic Regression
Logistic regression is a statistical method used for binary classification problems, where the outcome variable is categorical and typically takes on two values (e.g., 0 or 1). The core concept involv...
How It Works: The Mathematical Mechanism
The learning process in logistic regression involves finding the optimal parameters that maximize the likelihood of the observed data. This is typically performed using gradient descent or a...
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