null importance XGBoost
时间: 2024-06-05 16:09:00 浏览: 96
Null importance in XGBoost refers to the feature importance score assigned to features that are permuted randomly during the training process. This is done to determine the extent to which a feature contributes to the predictive performance of the model. If a feature has a high null importance score, it means that the feature doesn't contribute much to the model's performance and can be dropped without much impact on the model's accuracy.
The null importance score is calculated by training the model on the original dataset and then permuting the values of a feature randomly to create a null dataset. The model is then trained on the null dataset and the difference in performance between the original and null datasets is used to calculate the null importance score.
The null importance score can be used to identify features that can be removed from the model to reduce its complexity and improve its speed. However, it is important to note that null importance should be used in conjunction with other feature selection techniques, such as recursive feature elimination, to ensure that the most important features are retained in the model.