Gradienttikasvosmallit

Gradienttikasvosmallit, often translated as gradient boosting models, are a class of machine learning algorithms used for regression and classification tasks. They are an ensemble method, meaning they combine the predictions of multiple simpler models, known as weak learners, to create a stronger, more accurate predictor.

The core idea behind gradient boosting is to build models sequentially. Each new model is trained to correct the errors made by the previous models. This correction process is guided by the gradient of a loss function, which quantifies the difference between the model's predictions and the actual values. By minimizing this loss function in a step-wise manner, the ensemble gradually improves its overall performance.

Commonly used weak learners in gradient boosting are decision trees, particularly shallow ones. These trees are typically grown one at a time, with each subsequent tree focusing on the residuals (the differences between the true values and the current ensemble's predictions) of the preceding trees. The learning rate, a hyperparameter, controls the contribution of each new tree to the overall model, preventing overfitting.

Popular implementations of gradient boosting include XGBoost, LightGBM, and CatBoost, each offering optimizations for speed and performance. These models are widely used due to their high accuracy and ability to handle complex datasets.