underfitting

the model suffered from severe underfitting and failed to capture the underlying patterns.

we noticed significant underfitting when evaluating the model on the test set.

underfitting often results from using a model that is too simple for the data.

to avoid underfitting, we increased the model complexity and added more features.

the linear regression model exhibited underfitting compared to the neural network.

underfitting leads to poor performance on both training and test data.

we checked for underfitting by plotting the training and validation loss curves.

regularization can sometimes exacerbate underfitting if applied too aggressively.

the goal is to find a balance and avoid both underfitting and overfitting.

underfitting can be a consequence of insufficient training data or a poor feature set.

we used cross-validation to diagnose the extent of underfitting in the model.