Build and evaluate a Colab classification model
Company: Nextdoor
Role: Software Engineer
Category: Machine Learning
Difficulty: hard
Interview Round: Technical Screen
In Google Colab, design and implement an end-to-end classification workflow on a tabular dataset: describe how you would perform data loading, EDA, feature preprocessing (handling missing values, scaling/encoding), train/validation split, model selection (baseline vs. stronger models), hyperparameter tuning, and evaluation with appropriate metrics (choose metrics and justify). Show how you would address class imbalance, prevent leakage, use cross-validation, and report confidence intervals. Provide code or pseudocode structure, discuss trade-offs of algorithms you consider, and explain how you would interpret results and iterate.
Quick Answer: Build and evaluate a Colab classification model evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.