Improvements to the M5 model
The standard M5 algorithm tree currently has been received as the most state-of-the-art model among decision trees for completing complex regression tasks. This is mainly because of the accurate results it yields as well as its ability to handle tasks with a very large number of dimensions with upwards of hundreds of attributes.
In an attempt to improve on or otherwise optimize the standard M5 algorithm, M5Flex has recently been introduced as perhaps the most viable option. The M5Flex algorithm approach will attempt to augment a standard M5 tree model with domain knowledge. In other words, M5Flex empowers someone who has familiarity with the data population to review and choose the split attributes and split values for those important nodes (within the model tree) with the assumption that, since they may "know best," the resulting model will be even more accurate, consistent, and appropriate for practical applications than it would be by relying exclusively on...
