Why can accuracy be misleading?

If 95% of samples are class A, a trivial classifier predicting A always achieves 95% accuracy. Balanced accuracy and F1 are more informative for skewed datasets.

How is multi-class accuracy computed?

Sum of all correct predictions divided by total samples. Macro-averaged per-class accuracy weights each class equally regardless of support.

Accuracy from Confusion Matrix Calculator

Compute overall classification accuracy = (TP+TN)/(TP+TN+FP+FN) from a binary confusion matrix. Includes per-class accuracy breakdown.

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

Compute overall classification accuracy = (TP+TN)/(TP+TN+FP+FN) from a binary confusion matrix. Includes per-class accuracy breakdown

Each component has a specific meaning:

Binary confusion matrix — The binary confusion matrix recorded for the patient or scenario being assessed.

Note: Interpret the accuracy (confusion matrix) result against the clinical thresholds and context described above.

How to Use

Enter the binary confusion matrix for the patient or scenario you are assessing. Compute overall classification accuracy = (TP+TN)/(TP+TN+FP+FN) from a binary confusion matrix. Includes per-class accuracy breakdown. Use the accuracy (confusion matrix) result to inform your clinical assessment.