Gradient Boosting

Category: science

An ensemble technique that builds models sequentially, each trying to fix the errors of the previous one.

Boosting is the "team-improvement" model. The first model makes mistakes; the second one is programmed to focus only on the mistakes of the first. By building 100 models, each one better than the last, you end up with a terrifyingly accurate final output.

Common Examples

• Gradient boosting algorithms like XGBoost consistently produce the highest accuracy results for our complex tabular financial datasets.
• The iterative correction loop makes gradient boosting significantly more powerful, though more computationally intensive, than Random Forest methods.