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Predictions for 1 that were actually 1 Predictions for 0 that were actually 1
appear in the cell. Implying prediction appear in the cell. Implying prediction
that the loan will be approved and the that the loan will not be approved
loan was approved actually. Predicted and the loan was approved actually.
Confusion Matrix
Yes No
Yes 20 10
Actual
No 15 5
Predictions for 1 that were actually 0 Predictions for 0 that were actually 0
appear in the cell. Implying prediction appear in the cell. Implying prediction
that the loan will be approved and the that the loan will not be approved and
loan was not approved actually. the loan was not approved actually.
Brainy Fact
Mammography is the most effective way to detect breast cancer, but it can miss it 20% of the time. AI can help
improve the accuracy of breast cancer detection and prediction. An AI model developed by researchers at MIT’s
Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic for Machine Learning. Mirai is
a complex neural network that can detect breast cancer up to five years before it occurs.
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. Let’s fill the given matrix based on the table
given here.
Predicted Value Actual Value
Yes Yes
No Yes
Yes No
No No
Yes Yes
Yes No
No Yes Predicted
Confusion Matrix
Yes Yes Yes No
No No Yes
Actual
No No No
148 Artificial Intelligence Play (Ver 1.0)-X

