Chi tiết về thư mục
| Tác giả chính: |
Barros, Bruno de Mattos |
| Ngày xuất bản: |
2022 |
| Định dạng: |
Master thesis
|
| Ngôn ngữ: |
por |
| Nguồn: |
Repositório Institucional da UFG |
| Download full: |
http://repositorio.bc.ufg.br/tede/handle/tede/12454
|
Tóm tắt: |
Academic dropout is a problem that affects many public and private university students in Brazil and around the world. Machine learning techniques have been used to mitigate the problem, but still require a lot of manual adjustments. We present in this work, a proposal of an automatic machine learning framework to predict academic dropout, with the goal of obtaining good results without the need for human intervention. This data processing framework includes the following stages: pre-processing, feature vector creation, data splitting into testing and training sets, clustering of data from different degrees for training, model selection, model parameter tunning and explainability. Additionally, we formalize temporal data splitting approaches for train and test datasets, as this task is not adequately addressed in most of the previous works. |
