ML Training Reports
Complete reports of the Machine Learning training phase. Each subfolder contains the reports for experiments with different feature subset or number of classes. Classifier and regression reports will show different information, as appropriate metrics differ across the two types of tasks. - : Subfolders will have this naming convention. will describe the used feature categories, one letter representing the initial for each one. indicates the number of distinct quality classes. For example, CSRHN6 lists the reports for models trained with Content, Style, Readability, History and Network features, and 6 levels of quality (all features and all classes). The report name will indicate the used algorithm, as defined below. - ada_c: Ada Boost Classifier - ada_r: Ada Boost Regressor - forest_c: Random Forest Classifier - forest_r: Random Forest Regressor - gboost_c: Gradient Boosting Classifier - gboost_r: Gradient Boosting Regressor - gnb_c: Gaussian Naive Bayes Classifier - knn_c: K-Nearest Neighbors Classifier - linreg_r: Linear Regression - logreg_c: Logistic Regression - mlp_c: Multi-layer Perceptron Classifier - mlp_r: Multi-layer Perceptron Regressor - svc_c: Support Vector Classifier - svr_r: Support Vector Regressor - tree_c: Decision Tree Classifier - tree_r: Decision Tree Regressor
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Información adicional
| Campo | Valor |
|---|---|
| Última actualización de los datos | 27 de junio de 2022 |
| Última actualización de los metadatos | 27 de mayo de 2024 |
| Creado | 27 de junio de 2022 |
| Formato | ZIP |
| Licencia | Creative Commons Attribution |
| Datastore active | False |
| Has views | False |
| Id | 97cc2fc9-4087-4b9b-b8f3-1c9979d5cf00 |
| Package id | 24f17b48-304f-4c07-8f5d-2c9b62e25730 |
| Position | 5 |
| State | active |
| Url type | upload |