  Automatic Machine Learning (Auto-ML) has attracted more and more attention in
recent years, our work is to solve the problem of data drift, which means that
the distribution of data will gradually change with the acquisition process,
resulting in a worse performance of the auto-ML model. We construct our model
based on GBDT, Incremental learning and full learning are used to handle with
drift problem. Experiments show that our method performs well on the five data
sets. Which shows that our method can effectively solve the problem of data
drift and has robust performance.
