Implement decision tree from scratch

Last updated: January 19, 2026

Quick Overview

Write a clean implementation of k-means without using ML libraries.

JPMorgan

Machine Learning

Data Scientist

JPMorgan

January 19, 2026

Data Scientist

Onsite

Machine Learning

Medium

4,103 solved

Write a clean implementation of k-means without using ML libraries.

JPMorgan asks this during the Onsite 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

Ensemble methods (bagging, boosting, stacking)

Overfitting and underfitting

Class imbalance handling

Model interpretability and explainability

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

What regularization technique would you use and why?
How would you explain this model's predictions to a non-technical stakeholder?
What are the computational costs of this approach at scale?

Sharpen Your Skills on Codemia

Practice similar problems with our interactive workspace, get AI feedback, and track your progress.

Explore ML Interview Prep

Sample Answer

Core Concept: Decision Trees

Decision trees are a type of supervised learning model used for classification and regression tasks. They work by recursively splitting the data into subsets based on feature values to create a tree-l...

How It Works: Mathematical Foundations

The construction of a decision tree involves several mathematical components. The most common metrics used are Gini impurity, defined as:

$Gini(D) = 1 - \sum_{i=1}^{c} (p_i)^2$

where $p_i$ is ...

Submit Your Answer

Markdown supported

JPMorgan Data Scientist Interview Guide

Interview process, tips, and preparation timeline