Implement decision tree from scratch
Last updated: April 3, 2026
Quick Overview
Write a clean implementation of logistic regression without using ML libraries.
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April 3, 20265
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3,164 solved
Write a clean implementation of logistic regression without using ML libraries.
This ML question from Zoom's Take-home Project 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 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 detect and handle concept drift?
- When would you prefer a simpler model over a complex one?
- 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 Basics
Logistic Regression is a statistical method for predicting binary classes. The core idea is to model the probability that a given input belongs to a particular category using the logistic function, al...
How It Works: Mathematical Mechanism
In implementing logistic regression from scratch, the optimization of the coefficients is commonly performed using Maximum Likelihood Estimation (MLE). The likelihood function for logistic...
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