Confusion Matrix

Processing

This brick generates a Confusion Matrix visualization to evaluate the performance of a classification model. It compares the "True" labels (what the data actually represents) against the "Predicted" labels (what your model guessed).

The result is a grid (heatmap) that shows how often the model was correct and where it made mistakes. For example, it can show you how many times an image of a "Cat" was correctly identified as a "Cat" versus incorrectly identified as a "Dog."

Inputs

y true

The list or column containing the actual, correct labels (ground truth). These are the values you know are correct.

y pred

The list or column containing the labels predicted by your model. This must correspond index-by-index to the y true input (i.e., the first prediction corresponds to the first true value).

Inputs Types

You can check the list of supported types here: Available Type Hints.

Outputs

y true

The original list of true labels, passed through to the next brick unchanged.

y pred

The original list of predicted labels, passed through to the next brick unchanged.

Outputs Types

You can check the list of supported types here: Available Type Hints.

Options

The Confusion Matrix brick contains some changeable options:

Labels Size

Controls the font size of the text on the X and Y axes (the label names). Increasing this makes the categories easier to read.

Count Size

Controls the font size of the numbers displayed inside the grid squares.

Color Schema

Determines the color palette used for the heatmap. Darker colors typically represent higher counts/frequencies.

• blues: Shades of blue.
• yellowgreenblue: A gradient from yellow to green to blue.
• lightmulti: A light, multi-colored scheme.
• lighttealblue: Shades of teal and blue.
• bluegreen: Shades of blue and green.
• orangered: Shades of orange and red.
• redpurple: Shades of red and purple.