"They should be evaluated both on the training dataset, to give an idea of how well the model is “learning”, and the validation dataset, to give an idea of how well the model is “generalising.”"
"The shape and dynamics of a learning curve can be used to characterise the learning behaviour of a machine learning model and can in turn suggest the type of configuration changes that may be made to improve learning and/or performance. There are three common patterns that you are likely to observe in learning curves:"
"There are many different techniques that can be implemented to correct any under- or overfitting tendencies of a machine learning model. More information about these can be found on the respective articles about underfitting and overfitting."
"underfitting: better performance on the validation set than on the training set; the network is struggling with learning the patterns in the data"
"overfitting: high performance on a training set, significantly lower performance on a validation set; the network memorises the training samples instead of learning the patterns, i.e. struggles to generalise"