Classification predictive modeling problems involve predicting a class label for a given set of inputs. It is a challenging problem in general, especially if little is known about the dataset, as there are tens, if not hundreds, of machine learning algorithms to choose from. The problem is made significantly more difficult if the distribution of examples across the classes is imbalanced. This requires the use of specialized methods to either change the dataset or change the learning algorithm to handle the skewed class distribution. A common way to deal with the overwhelm on a new classification project is to use a favorite machine learning algorithm like Random Forest or SMOTE. Another common approach is to scour the research literature for descriptions of vaguely similar problems and attempt to re-implement the algorithms and configurations that are described. These approaches can be effective, although they are hit-or-miss and time-consuming respectively.

Many machine learning models are capable of predicting a probability or probability-like scores for class membership. Probabilities provide a required level of granularity for evaluating and comparing models, especially on imbalanced classification problems where tools like ROC Curves are used to interpret predictions and the ROC AUC metric is used to compare model performance, both of which use probabilities. Unfortunately, the probabilities or probability-like scores predicted by many models are not calibrated. This means that they may be over-confident in some cases and under-confident in other cases. Worse still, the severely skewed class distribution present in imbalanced classification tasks may result in even more bias in the predicted probabilities as they over-favor predicting the majority class. As such, it is often a good idea to calibrate the predicted probabilities for nonlinear machine learning models prior to evaluating their performance.