Implement decision tree from scratch
Last updated: February 24, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
Redfin
February 24, 20269
6
3,437 solved
Write a clean implementation of logistic regression without using ML libraries.
Redfin asks this during the Take-home Project 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 handle a highly imbalanced dataset?
- What are the computational costs of this approach at scale?
- How would you detect and handle concept drift?
- When would you prefer a simpler model over a complex one?
Sharpen Your Skills on Codemia
Practice similar problems with our interactive workspace, get AI feedback, and track your progress.
Explore ML Interview PrepSample Answer
Core Concept: Decision Trees
A decision tree is a supervised learning algorithm that is used for both classification and regression tasks. It works by breaking down a dataset into smaller subsets while at the same time developing...
How It Works: Mathematical Foundations
The splitting criterion is often based on metrics such as Information Gain or Gini Impurity. For information gain, the formula is:
[ IG(T, A) = H(T) - \sum_{v \in Values(A)} \frac{|T_v|}{|T|} H(T_v)...
Submit Your Answer
