4x4 Confusion Matrix Calculator

Confusion Matrix Metrics:

\[ \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} \] \[ \text{Precision} = \frac{TP}{TP + FP} \] \[ \text{Recall/Sensitivity} = \frac{TP}{TP + FN} \] \[ \text{Specificity} = \frac{TN}{TN + FP} \] \[ F1\text{-score} = 2 \times \frac{Precision \times Recall}{Precision + Recall} \]

Actual vs Predicted Values

	Predicted Class 1	Predicted Class 2	Predicted Class 3	Predicted Class 4
Actual Class 1
Actual Class 2
Actual Class 3
Actual Class 4

Results

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1. What is a 4x4 Confusion Matrix?

A 4x4 confusion matrix is a table that visualizes the performance of a classification model with four classes. It shows how many predictions were correct (diagonal) and incorrect (off-diagonal) for each class.

2. How Does the Calculator Work?

The calculator computes various metrics for each class:

Where:

\( TP \) — True Positives (correct predictions for the class)
\( FP \) — False Positives (incorrect predictions for the class)
\( TN \) — True Negatives (correct predictions for other classes)
\( FN \) — False Negatives (incorrect predictions for other classes when actual was this class)

3. Importance of Confusion Matrix Analysis

Details: Confusion matrices help identify which classes are being confused with others, allowing for targeted model improvement. They provide more insight than simple accuracy metrics.

4. Using the Calculator

Tips: Enter the counts of actual vs predicted classifications in the 4x4 grid. The calculator will compute metrics for each class and overall accuracy.

5. Frequently Asked Questions (FAQ)

Q1: What's the difference between precision and recall?
A: Precision measures how many selected items are relevant, while recall measures how many relevant items are selected.

Q2: When should I use F1-score?
A: F1-score is useful when you want to balance precision and recall, especially with imbalanced datasets.

Q3: What does specificity measure?
A: Specificity measures the proportion of actual negatives that are correctly identified.

Q4: How do I interpret off-diagonal elements?
A: Off-diagonal elements show misclassifications - which classes are being confused with which others.

Q5: Can I use this for binary classification?
A: While you can, a 2x2 matrix would be simpler for binary classification cases.