New release of perClass Mira brings many enhancements such as confusion matrix visualization of error structure, interactive performance fine-tuning, and RGB preview mode.
Understanding performance of any machine learning solution is of fundamental importance. Therefore, perClass Mira 1.1 introduces confusion matrix visualization visualizing error brake-down within individual classes. Both normalized and absolute confusion matrices are available together with convenient additional information such as per-class errors or precision (ratio of a correct acceptance and all deciions of a given class).
Because static performance visualization does not allow us to improve, we have developed a fully interactive confusion matrix for perClass Mira. User may lower errors at specific entries (or for the entire class) using sliders. In addition, multiple constraints may be easily created by double-clicking the entries. Then only solutions fulfilling all constraints are considered. Constraints may be adjusted live (via Ctrl+mouse wheel) and enabled/disabled at will. This provides a strong yet easy-to-use environment for performance fine-tuning. Any changes in the confusion matrix are directly reflected in the model decisions. In this way, we may quickly create solutions fulfilling specific application requirements.
Additional new functionality in the 1.1 release:
Want to try out this new functionality? Request a demo!