Gradientforsterkning

Gradientforsterkning, often translated as gradient boosting, is a machine learning technique used for both regression and classification tasks. It is a powerful ensemble method that builds a predictive model in a stage-wise fashion. Unlike bagging methods, which build trees independently and average their predictions, gradient boosting combines weak learners sequentially.

The core idea of gradient boosting is to iteratively add new models to the ensemble, with each new model attempting to correct the errors made by the previous ones. Specifically, it trains each new model to predict the negative gradient of the loss function with respect to the predictions of the current ensemble. This means that at each step, the algorithm focuses on the data points where the current model is performing poorly.

The process starts with a simple initial model, often a single decision tree. Then, in each subsequent iteration, a new weak learner (typically another decision tree, but can be other models) is trained on the residuals or errors of the combined predictions of all previously trained models. These residuals are essentially the negative gradient of the loss function. The new model's contribution is then scaled by a learning rate, which helps to prevent overfitting by controlling the impact of each new tree.

Gradient boosting is known for its high predictive accuracy. However, it can be computationally intensive and is prone to overfitting if not properly tuned with appropriate regularization techniques, such as controlling the depth of the trees, the number of trees, and the learning rate. Popular implementations include XGBoost, LightGBM, and CatBoost, which offer various optimizations for speed and performance.