Math Calculator Ai

AI Model Performance Calculator

AI Model Performance Metrics:

Accuracy:

Precision:

Recall (Sensitivity):

F1-Score:

Understanding AI Model Performance with Key Metrics

When developing or evaluating Artificial Intelligence (AI) models, especially for classification tasks, it's crucial to understand how well they perform. A simple "accuracy" score often isn't enough to get a complete picture. This calculator helps you compute essential performance metrics based on the outcomes of your binary classification model.

What are Binary Classification Outcomes?

In binary classification, a model predicts one of two classes (e.g., "positive" or "negative," "spam" or "not spam," "disease" or "no disease"). The actual outcomes compared to the model's predictions fall into four categories:

• True Positives (TP): The model correctly predicted the positive class. (e.g., predicted spam, and it was spam)
• True Negatives (TN): The model correctly predicted the negative class. (e.g., predicted not spam, and it was not spam)
• False Positives (FP): The model incorrectly predicted the positive class. This is also known as a Type I error. (e.g., predicted spam, but it was not spam)
• False Negatives (FN): The model incorrectly predicted the negative class. This is also known as a Type II error. (e.g., predicted not spam, but it was spam)

Key Performance Metrics Explained:

These four outcomes form the basis for calculating several important metrics:

Accuracy

Accuracy measures the proportion of total predictions that were correct. It's a good general indicator but can be misleading in datasets with imbalanced classes.

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Example: If your model correctly identified 90 spam emails (TP) and 80 legitimate emails (TN), but also misclassified 10 legitimate emails as spam (FP) and 20 spam emails as legitimate (FN), your total instances are 90+80+10+20 = 200. Your accuracy would be (90+80)/200 = 170/200 = 0.85 or 85%.

Precision

Precision answers: "Of all instances predicted as positive, how many actually were positive?" High precision means fewer false positives. This is crucial when the cost of a false positive is high (e.g., flagging a legitimate transaction as fraudulent).

Precision = TP / (TP + FP)

Example: Using the numbers above, your precision would be 90 / (90 + 10) = 90 / 100 = 0.90 or 90%. This means 90% of the emails your model flagged as spam were indeed spam.

Recall (Sensitivity)

Recall answers: "Of all actual positive instances, how many did the model correctly identify?" High recall means fewer false negatives. This is crucial when the cost of a false negative is high (e.g., failing to detect a disease).

Recall = TP / (TP + FN)

Example: With the same numbers, your recall would be 90 / (90 + 20) = 90 / 110 ≈ 0.818 or 81.82%. This means your model caught about 81.82% of all actual spam emails.

F1-Score

The F1-Score is the harmonic mean of Precision and Recall. It provides a single metric that balances both precision and recall, which is particularly useful when you have an uneven class distribution. A high F1-Score indicates that the model has good values for both precision and recall.

F1-Score = 2 * (Precision * Recall) / (Precision + Recall)

Example: Using the calculated precision (0.90) and recall (0.818), your F1-Score would be 2 * (0.90 * 0.818) / (0.90 + 0.818) = 2 * 0.7362 / 1.718 ≈ 0.857 or 85.7%.

How to Use This Calculator

Simply input the number of True Positives, True Negatives, False Positives, and False Negatives from your AI model's performance evaluation. The calculator will instantly provide you with the Accuracy, Precision, Recall, and F1-Score, helping you quickly assess and compare different models or iterations of your current model.