Underfitting возникает, когда модель слишком проста или недостаточно обучена, чтобы уловить значимые паттерны в данных. Недообученная модель плохо работает как на обучающих данных, так и на новых данных, поскольку не выучила базовые отношения, необходимые для задачи.
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.