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This is a hard difficulty Machine Learning question, commonly asked during Take-home Project rounds at Qube.

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This question is commonly asked for Data Scientist candidates at Qube during technical interviews.

Implement and Tune KNN Classifier

Last updated: May 6, 2026

Quick Overview

This question evaluates a candidate's competency in implementing and tuning the K-nearest neighbors algorithm, covering data preprocessing, distance-based classification, model evaluation, and hyperparameter selection.

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Implement and Tune KNN Classifier

Qube

Jan 12, 2026, 12:00 AM

hardData ScientistTake-home ProjectMachine Learning

You are given two CSV files for a three-class leaf-classification task.

train.csv contains three numeric feature columns: feature_0 , feature_1 , and feature_2 , plus a class label column.
unlabeled.csv contains the same three feature columns but no labels.

Complete the task in a Jupyter notebook:

Load and inspect the labeled dataset.
Split the labeled data into training and testing sets.
Implement a K-nearest neighbors classifier from scratch. You may use standard data-processing libraries, but the KNN prediction logic should be your own implementation.
Train the classifier and evaluate its accuracy on the held-out test set.
Tune hyperparameters, especially the value of k , and try to improve accuracy. You may use cross-validation.
Use the final model to predict labels for unlabeled.csv .
Write the predictions to an output CSV file.

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