Solving Imbalanced Regression Tasks
Root Mean Squared Error
Obtain the relevance of data points
Non-Standard Evaluation Metrics
Squared Error-Relevance Area (SERA)
Model Variance
Plot of phi versus y and boxplot of y
Model Bias
Pearson's Correlation
Predictive Modelling Evaluation Statistics
Standard Evaluation Metrics
Mean Squared Error
Generation of relevance function
Relevance function for extreme target values
Custom Relevance Function
Imbalanced domain learning has almost exclusively focused on solving classification tasks, where the objective is to predict cases labelled with a rare class accurately. Such a well-defined approach for regression tasks lacked due to two main factors. First, standard regression tasks assume that each value is equally important to the user. Second, standard evaluation metrics focus on assessing the performance of the model on the most common cases. This package contains methods to tackle imbalanced domain learning problems in regression tasks, where the objective is to predict extreme (rare) values. The methods contained in this package are: 1) an automatic and non-parametric method to obtain such relevance functions; 2) visualisation tools; 3) suite of evaluation measures for optimisation/validation processes; 4) the squared-error relevance area measure, an evaluation metric tailored for imbalanced regression tasks. More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.