Confusion Matrix

A table that visualizes the performance of a classification algorithm, showing how often it correctly or incorrectly predicts different categories.

• Rows: Represent the "True" or actual categories of the data.

• Columns: Represent the "Predicted" categories assigned by the skill.

• Numbers within the cells: Indicate the count of how many instances fall into each combination of True and Predicted categories.

• Diagonal Cells (from top right to bottom left):

  • These represent the cases where the True category matches the Predicted category - these are the correct predictions.

  • The higher the numbers in these cells, the better your model is at classifying those specific categories.

• Off-Diagonal Cells:

  • These represent the cases where the True category and Predicted category don't match - these are the errors or misclassifications.

  • Analyzing these cells helps you understand which categories your model tends to confuse with each other.