The Confusion Matrix is a table that summarizes the performance of a classification model by showing the number of correct and incorrect predictions for each class (lived or died), visualized using a heatmap to make it easier to interpret. This visualization helps in understanding how well the model is performing, particularly in identifying how many True Positive (top-left cell), True Negative (bottom-right cell), False Positive (bottom-left cell), and False Negative (top-right cell) predictions were made.

Conclusions drawn from this visualization:

• High True Positives (65): The model performs well at predicting horses that will die.
• Moderate False Positives (15): There are some inaccurate forecasts where the model predicts survival for horses that actually died.
• Moderate False Negatives (17): TThere are some inacurate forecasts where the model predicts death for horses that actually lived.
• High True Negatives (58): The model performs well at predicting horses that will survive.