Le sous-apprentissage se produit lorsqu’un modèle est trop simple ou insuffisamment entraîné pour saisir les motifs significatifs des données. Un modèle sous-appris obtient de mauvaises performances à la fois sur les données d’entraînement et sur les nouvelles données, car il n’a pas appris les relations sous-jacentes nécessaires à la tâche.
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.