Implement decision tree from scratch
Last updated: July 16, 2025
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
Elastic
July 16, 202535
6
4,961 solved
Write a clean implementation of logistic regression without using ML libraries.
Elastic asks this during the Technical 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 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 explain this model's predictions to a non-technical stakeholder?
- How would you ensure reproducibility in your ML pipeline?
- How would you handle a highly imbalanced dataset?
- 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 for predicting binary classes. It is used when the outcome variable is categorical (e.g., yes/no, spam/not spam). Unlike linear regression, which predicts c...
How It Works: Mathematical Mechanism
The logistic regression model uses maximum likelihood estimation (MLE) to find the best-fitting parameters. The likelihood function is defined for a set of independent observations, and we maximize it...
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