A confusion matrix is a performance measurement technique for Machine learning classification. It is a kind of table which helps you to the know the performance of the classification model on a set of test data for that the true values are known

The confusion matrix visualizes the accuracy of a classifier by comparing the actual and predicted classes. The binary confusion matrix is composed of squares:

  • TP: True Positive: Predicted values correctly predicted as actual positive
  • FP: Predicted values incorrectly predicted an actual positive. i.e., Negative values predicted as positive
  • FN: False Negative: Positive values predicted as negative
  • TN: True Negative: Predicted values correctly predicted as an actual negative

You can compute the accuracy test from the confusion matrix:



Why GUI App in Docker?

GUI in docker has its own advantage, when you want to test a GUI application with different versions you can use docker containers instead of host operating system and pollute it with new packages.

This approach also helps you avoid any incompatibilities with other packages in your environment. If you need to temporarily run two versions of a program, you can use Docker to avoid having to remove and reinstall the software on your host.