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5.1.1 Confusion Matrix

The confusion matrix is a critical evaluation tool in classification models,
providing a visual representation of the performance of the predictive model.

It is a table used to describe the performance of a classification model on a

set of data for which the true values are known. The matrix itself is segmented
into four parts: true positives (TP), true negatives (TN), false positives (FP),

and false negatives (FN). True positives and true negatives represent the
observations that are correctly predicted as occurring (stroke) and not

occurring (no stroke), respectively. Conversely, false positives occur when
the model incorrectly predicts an event (stroke) when it did not happen, while

false negatives are cases where the model fails to predict an event that

actually occurred. Table 5.1 shows the result table of the confusion matrix
and Figure 5.1 depicts the confusion matrix heatmap.

Table 5.1 Result of Confusion Matrix

Actual Class

Positive Negative

Positive TP = 474 FP = 190

Predicted Class
Negative FN = 65 TN= 570

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