Comments (1)
Dguipla
commented on July 28, 2024
Se ha completado todo el codigo para todos los clasificadores base trabajando con el conjunto de datos BCW.
Se ha abstraido todo lo que es la creacion de los modelos para cada clasificador para poder reutilizar el pipeline con los diferentes conjuntos de datos
from tfm-semisup.
Related Issues (20)
- SelfTrainning - Calculos de probabilidades por los clasificadores base
- Nueva Clase para SelfTraining
- Self Trainning --> Implementacion de pipeline para cada Conjunto de datos + modelo SelfTrainnig
- SelfTraining --> Lanzamiento de todos los Pipelines + SelfTraining- modelos (para cada clasificador :DT, LR....)
- Crear la clase del "desetiquetador" HOT 16
- Crear Class para los datos de SemiSupervisado
- modificacion de los for/s de las funciones calculo de resultados por maps
- CoTraining HOT 1
- Parte 4 (Co-Training Implementacion)
- Duda - Paper Triguero-Garcia-Herrera (Keel) y sus implementaciones para poder comparar con Spark y Self/CoTraining HOT 1
- Overleaf - Memoria - Compartida
- Parte 5 Resultados
- Comparativa Self/CoTraining Spark con Keel (paper Triguero)
- Parte 5 (Resultados y Memoria Final)
- Reunión seguimiento
- Scatter - Duda (Keel vs Spark) HOT 3
- Memoria
- Completar resultados con powerBI
- Crear una biblioteca de Spark HOT 14
- Dashboard publicado online con PowerBI HOT 2
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