Implement logistic regression from scratch
Last updated: February 27, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
Palantir
February 27, 20260
5
253 solved
Write a clean implementation of logistic regression without using ML libraries.
Palantir asks this during the Phone Screen to assess your depth in ML. They expect you to discuss the mathematical foundations, practical considerations, and common pitfalls when applying these techniques 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 explain this model's predictions to a non-technical stakeholder?
- What are the computational costs of this approach at scale?
- How would you ensure reproducibility in your ML pipeline?
- 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 statistical method used for binary classification problems. It predicts the probability that a given input belongs to a certain class using the logistic function, also known a...
How it Works: Gradient Descent and Optimization
To implement logistic regression from scratch, we typically use gradient descent to optimize the coefficients. The cost function used is the negative log-likelihood, defined as:
[ J(\beta) = -\frac{...
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