Misclassification Rate Calculator
Use this calculator to determine the misclassification rate (also known as the error rate) of a classification model based on the confusion matrix values. Enter your True Positives, True Negatives, False Positives, and False Negatives below.
Results
What is Misclassification Rate?
The Misclassification Rate, often referred to as the Error Rate, is a fundamental metric used in machine learning and statistics to evaluate the performance of a classification model. It represents the proportion of predictions that the model got wrong.
While "Accuracy" measures how many predictions were correct, the Misclassification Rate measures the opposite: how often the model is confused or incorrect.
The Formula
The misclassification rate is calculated using the four components of a confusion matrix:
- True Positives (TP): Correctly identified positive cases.
- True Negatives (TN): Correctly identified negative cases.
- False Positives (FP): Negative cases incorrectly identified as positive.
- False Negatives (FN): Positive cases incorrectly identified as negative.
Alternatively, since the sum of the Misclassification Rate and Accuracy always equals 1 (or 100%), it can be calculated as:
Why is it Important?
In many real-world scenarios, knowing how often a model fails is just as critical as knowing how often it succeeds. For example:
- Medical Diagnosis: A high misclassification rate means potentially missing sick patients (False Negatives) or diagnosing healthy people incorrectly (False Positives).
- Spam Filtering: Misclassifying a legitimate email as spam (False Positive) is a nuisance, while letting spam through (False Negative) is a failure of the filter.
Example Calculation
Imagine a credit card fraud detection model tested on 200 transactions:
- True Positives: 40 (Fraud correctly caught)
- True Negatives: 140 (Legitimate transactions correctly allowed)
- False Positives: 15 (Legitimate transactions flagged as fraud)
- False Negatives: 5 (Fraudulent transactions missed)
Step 1: Calculate Total Errors = 15 + 5 = 20.
Step 2: Calculate Total Observations = 40 + 140 + 15 + 5 = 200.
Step 3: Calculate Rate = 20 / 200 = 0.10.
The misclassification rate is 10%.