When should I use F1 over accuracy?

Use F1 when classes are imbalanced. Accuracy can be misleadingly high if one class dominates; F1 balances precision and recall and penalises poor performance on the minority class.

What is a good F1 score?

Depends heavily on domain. In medical diagnosis F1 > 0.9 is expected. For general NLP tasks F1 > 0.8 is considered strong. Always compare against a domain baseline.

Precision, Recall & F1 Score Calculator

Calculate precision = TP/(TP+FP), recall = TP/(TP+FN), and F1 = 2×(P×R)/(P+R) from a confusion matrix. Essential metrics for evaluating classification model performance.

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

Calculate precision = TP/(TP+FP), recall = TP/(TP+FN), and F1 = 2×(P×R)/(P+R) from a confusion matrix. Essential metrics for evaluating classification model performance

Each component has a specific meaning:

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

Note: Interpret the precision / recall / f1 result against the clinical thresholds and context described above.

How to Use

Enter the confusion matrix for the patient or scenario you are assessing. Calculate precision = TP/(TP+FP), recall = TP/(TP+FN), and F1 = 2×(P×R)/(P+R) from a confusion matrix. Essential metrics for evaluating classification model performance. Use the precision / recall / f1 result to inform your clinical assessment.