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

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?

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

Citadel Data Scientist interview question: Design Framework for Robust House-Price Prediction Model. {"blocks": [{"key": "9d09f9d8", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...