Confusion Matrix & ML Metrics Calculator
Input True Positives, False Positives, True Negatives, and False Negatives to instantly compute Accuracy, Precision, Recall, and F1-Score.
Concept Breakdown: Supervised vs Unsupervised Model Training
Imagine you are hiking down a mountain in pitch-black fog. You feel the slope with your feet and step downward. Gradient Descent does this mathematically — taking steps downhill until reaching the valley floor of lowest error.
When an AI spots spam emails, it can make two mistakes: letting spam through (False Negative) or blocking a real email (False Positive). A Confusion Matrix tracks these tradeoffs.
Click "Step (1 Iteration)" below. Watch the ball take mathematical steps down the parabolic curve until it settles comfortably at the bottom minimum!
Quick Reference & Instructions
Simple steps, pro tips, and execution details
Provide Inputs
Type, paste, or select your values in the form fields below.
Instant Live Analysis
Calculations and formatting happen automatically with zero delay as you type.
Copy or Use Output
Copy results or apply the clean output directly to your projects.
How It Works
Computes complete binary classification metrics including Specificity, Balanced Accuracy, and Matthew's Correlation Coefficient (MCC).
Formula & Logic
Frequently Asked Questions
Common questions about calculations, assumptions, and edge cases.
Accuracy can look high on an imbalanced dataset just by predicting the majority class; F1-score balances precision and recall directly, which exposes weak performance on the minority class that accuracy can hide.
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