10Tree ensembles vs linear models vs neural networks, how do you choose for a customer's tabular problem?▼medium★ EssentialDatabricksGoogleSnowflake2 repliesunlockedThe empirical answer for tabular data hasn't changed in a decade, but interviewers want the why, and the two situations where the default is wrong. A decision table you can defend under panel Q&A.Open full answer →
11The customer wants to try a fancier model. You think the win is in feature engineering. Make your case, with examples.▼mediumDatabricksSnowflakeGoogle1 replies○ sign inOn enterprise data, the feature pipeline routinely buys 2-5x the lift of a model swap. Here's the worked churn example, and the leakage rule every engineered feature must pass, that makes the argument stick.Open full answer →
44Gradient boosting vs random forests: how does each build trees, the bias-variance behavior, and when to pick which?▼hardDatabricksGoogleSnowflake1 replies◆ premiumBoth are tree ensembles, but one averages independent deep trees to kill variance and the other adds shallow trees in sequence to kill bias. That single difference dictates the failure modes, the tuning burden, and which one you hand a customer who will retrain it after you leave.Open full answer →
