underfitting
Producing a model with poor predictive ability because the model hasn't fully captured the complexity of the training data.
Plain English Explanation
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.
How is it used?
Practitioners refer to underfitting when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.