Name: Nagulapati Rohith Kumar, Class ID: 16

Name: Nageswara Rao Nandigama, Class ID: 17

ICP ID: 6-2

source code: source code link

Question

1. Accuracy: Overall, how often is the classifier correct?

(TP+TN)/total

2. Misclassification Rate: Overall, how often is it wrong?

(FP+FN)/total equivalent to 1 minus Accuracy also known as "Error Rate"

3. True Positive Rate: When it's actually yes, how often does it predict yes?

TP/actual yes also known as "Sensitivity" or "Recall"

4. False Positive Rate: When it's actually no, how often does it predict yes?

FP/actual no

5. Specificity: When it's actually no, how often does it predict no?

TN/actual no equivalent to 1 minus False Positive Rate

6. Precision: When it predicts yes, how often is it correct?

TP/predicted yes

7. Prevalence: How often does the yes condition actually occur in our sample?

actual yes/total

Source Code snippet for calculating

Output:

confusion Matrix 4.0 1.0 3.0 2.0

Accuracy = 0.6

Error rate=0.4

Weighted True positive rate: 0.6000000000000001

Weighted false positive rate: 0.4

specificity=0.6

Weighted precision: 0.6190476190476191

Weighted recall: 0.6000000000000001

Prevalance=0.5