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Build the Confusion Matrix
To build the confusion matrix, we manually compare the predicted labels with the actual labels and categorise the
results into True Positives, True Negatives, False Positives, and False Negatives.
For example, predicting the possibility of snowfall. Here, Yes would mean there will be snowfall, and No would
mean that there will be no snowfall. So, the AI model will have output as Yes or No.
The following table shows the actual values and the predicted values:
Predicted Value Actual Value
Yes Yes
No Yes
Yes No
No No
Yes Yes
Yes No
No Yes
Yes Yes
No No
No No
Let’s fill the given matrix based on the table given here.
Predicted
Confusion Matrix
Yes No
Yes
Actual
No
Steps to Fill in the Confusion Matrix
• Count the number of rows having Yes in both the columns of the table and put the count of it in the top left cell.
• Similarly, the number of rows having Yes in the Actual Value column and No in the Predicted Value column
will be shown in the top right cell of the confusion matrix.
• Number of rows having No in the Actual Value and Yes in the Predicted Value column will be shown in the
down left cell of the confusion matrix.
• Lastly, the number of rows having No in the both columns will be shown in the down right cell of confusion
matrix.
230 Touchpad Artificial Intelligence (Ver. 3.0)-X

