El underfitting ocurre cuando un modelo es demasiado simple o está insuficientemente entrenado para capturar patrones significativos en los datos. Un modelo subajustado funciona mal tanto en datos de entrenamiento como en datos nuevos porque no ha aprendido las relaciones subyacentes necesarias para la tarea.
Producing a model with poor predictive ability because the model hasn't fully captured the complexity of the training data. Many problems can cause underfitting, including: - Training on the wrong set of features. - Training for too few epochs or at too low a learning rate. - Training with too high a regularization rate. - Providing too few hidden layers in a deep neural network. See Overfitting in Machine Learning Crash Course for more information.
Producing a model with poor predictive ability because the model hasn't captured the complexity of the training data.
A phenomenon in machine learning where a model is too simple to capture the underlying structure of the data, resulting in poor performance on both training data and new data.