Why is gradient boosting more commonly used in industrial environments than random forests?

Gradient boosting is more commonly used in industrial environments for several reasons:

1. High accuracy and flexibility. Gradient boosting builds models sequentially, correcting errors of previous ones, which allows better adaptation to complex data dependencies.

2. Customization and regularization options. Modern implementations (e.g., XGBoost, LightGBM, CatBoost) offer many parameters to combat overfitting and optimize performance.

3. Handling various data types and missing values. Gradient boosting can work with categorical features and missing values without complex preprocessing.

4. Interpretability. Despite its complexity, feature importance and influence can be analyzed, which is important for business applications.

Unlike random forests, which build many independent trees and average results, gradient boosting usually provides more accurate predictions, especially on complex tasks, but requires more careful tuning and more resources for training.

Thus, the choice in favor of gradient boosting is driven by a balance between model quality and its ability to adapt to real data and business requirements.