Gradient Boosted Models (GBMs) are powerful algorithms for supervised learning , but they are also prone to overfitting if not properly trained. Overfitting occurs when a model learns the noise in the training data instead of the underlying patterns, resulting in poor performance on new, unseen data.
To safeguard against overfitting in GBMs, the following techniques are very effective:
- Cross-validation: Use k-fold cross-validation to evaluate your model’s performance on different subsets of the training data. This can help you identify overfitting and choose the best hyperparameters for your model.
- Hyperparameter tuning: GBMs have a large number of hyperparameters that can be tuned to improve model performance. Hyperparameter tuning is a very important step in GBM model training process to reduce overfitting and increasing prediction accuracy. Please refer to this post, which goes into detail on GBM hyperparameters and tuning process
- Reduce model complexity: Simplify your model by reducing the number of features or decreasing the depth of the trees. This can help prevent overfitting by reducing the model’s ability to learn noise in the data.
- Increase training data: The more training data you have, the better your model can generalize to new data. Consider collecting more data or using data augmentation techniques to increase the size of your training set.
By implementing these techniques, you can reduce the risk of overfitting in your GBM and improve its ability to generalize to new data.