This slide will give you a basic understanding of Artificial Neural Network.

1. Data Mining

2. Confusion Matrix
Positive
(+)
Negative
(-)
Predicted
positive (Y)
TP FP
Predicted
negative (N)
FN TN
Confusion Matrix
2CSEDIU

3. Predicted
positive
Predicted
negative
Actual Positive
(TP) (FN)
Actual Negative
(FP) (TN)

4. Example:
• Among 150 samples there are 120 positive samples. A classifier
model predicts 80 positive and 16 negative samples correctly. Draw
the confusion matrix and find out True Positive Rate and Precision
from the confusion matrix.
Predicted
positive
Predicted
negative
total
Actual
Positive 80 40 120
Actual
Negative 14 16 30
total 94 56 150

5. FNFPTNTP
TNTP


Accuracy
40141690
1680



64.0

6. FNTP
TP

RatePositiveTrue
4080
80


%6.66

7. FPTP
TP

Precision
1480
80


%85