Choose and justify ML algorithms for tabular prediction

You need to pick an algorithm for tabular prediction of arrival delay given the following constraints: 500k rows, 120 features (mixed numeric and categorical, with missing values), non-linear interactions, a hard latency requirement of under 100 ms per prediction, and a need for instance-level explanations for operations. Choose among linear/regularized regression, a single decision tree, Random Forest, and XGBoost, and defend your choice:

1. Outline a comparison plan (feature preprocessing, handling categorical variables, guarding against leakage, time-aware cross-validation) and the metrics used for selection (RMSE, calibration, latency, memory).

2. Make the case for when linear regression outperforms trees (bias/variance, extrapolation, monotonic constraints) and when trees/ensembles are the better fit (non-linearities, interactions).

3. Compare Random Forest and XGBoost thoroughly: training and inference cost, how sensitive each is to noisy features, risk of overfitting, how to handle class/label imbalance in a regression setting, robustness to missing values, the hyperparameters that most influence bias/variance, and situations in practice where RF might beat XGBoost.

4. Explain how you would generate stable, fast explanations (for example, TreeSHAP versus permutation), carry out fairness checks, and calibrate predictions.

5. Define an experiment design that lets you pick a winner with confidence (stratified, time-split cross-validation, paired tests on fold errors, and confirmation on a holdout set).

Overview

This question tests a candidate's ability in model selection for tabular regression—balancing trade-offs across linear/regularized regression, decision trees, Random Forest, and XGBoost—as well as skills in preprocessing, categorical handling, missingness, latency and memory constraints, instance-level explainability, fairness checks, calibration, and experiment design. It falls under the Machine Learning domain and is often used to assess both conceptual understanding of model behavior and the bias–variance trade-off, plus practical application skills in validation strategy, performance/latency measurement, and production-ready explainability and robustness.