Model evaluation is an important part of a training process since it ensures that a model has been properly trained on the existing dataset and can be used to make predictions for an unseen dataset.
This notebook describes how to compute and derive insights from various classification evaluation metrics such as accuracy, recall, precision, f1-score, precision-recall curve and receiver operating characteristic curve.