What is the difference between sensitivity and specificity?

Sensitivity (TPR) = TP/(TP+FN) measures how well the classifier detects positives. Specificity (TNR) = TN/(TN+FP) measures how well it rejects negatives. High sensitivity minimises missed positives.

How are PPV and NPV affected by prevalence?

PPV and NPV depend on disease/class prevalence. At low prevalence even a high-sensitivity test has poor PPV because false positives outnumber true positives.

Confusion Matrix TPR/FPR Breakdown Calculator

Derive sensitivity (TPR), specificity (TNR), false positive rate (FPR), false negative rate (FNR), positive predictive value (PPV), and negative predictive value (NPV) from raw confusion matrix counts.

Networking

Algorithms

Binary & Number

Systems

Dev Metrics

IP Subnet Calculator

IP Address

CIDR Prefix

Network

192.168.1.0

Broadcast

192.168.1.255

Subnet Mask

255.255.255.0

First Host

192.168.1.1

Last Host

192.168.1.254

Usable Hosts

254

Binary breakdown:

IP: 11000000.10101000.00000001.00000000

Mask: 11111111.11111111.11111111.00000000

Net: 11000000.10101000.00000001.00000000

How It Works

Each component has a specific meaning:

Raw confusion matrix counts — The raw confusion matrix counts recorded for the patient or scenario being assessed.

Note: Interpret the confusion matrix breakdown result against the clinical thresholds and context described above.

How to Use

Enter the raw confusion matrix counts for the patient or scenario you are assessing. Derive sensitivity (TPR), specificity (TNR), false positive rate (FPR), false negative rate (FNR), positive predictive value (PPV), and negative predictive value (NPV) from raw confusion matrix counts. Use the confusion matrix breakdown result to inform your clinical assessment.