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Design Framework for Robust House-Price Prediction Model

CitadelData Scientist · Technical Screen
HardData ScientistTechnical ScreenMachine Learning

Last updated

Evaluates robust house-price prediction modeling across diagnostics, features, scale, and validation. Strong answers cover linear regression diagnostics, Cook's distance, leverage, Random Forest complexity controls and variable importance, housing-market feature engineering, leakage prevention, large-scale training, and time-geography validation.

Scenario

Deep-dive on model robustness and feature engineering for house-price prediction.

Question

In linear regression, how do you detect and handle outliers and influential points? Explain Cook's distance and high-leverage diagnostics. In Random Forests, how can you prune trees and compute variable importance? Design a modelling framework to predict a city's house prices. Which factors would you include and why? When the number of predictors is huge, how can you compute or update the β coefficients of a linear regression in mini-batches without loading all data at once?

Hints

Discuss leverage-residual plots, robust loss, OOB importance, incremental least squares or SGD.