What Is The Formula For Accuracy

Accuracy Formula:

\[ \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} \]

True Positives (TP):

True Negatives (TN):

False Positives (FP):

False Negatives (FN):

Accuracy:

Accuracy (%):

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1. What is Classification Accuracy?

Definition: Accuracy is a metric that measures the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined.

Purpose: It provides a simple way to evaluate the performance of a classification model in machine learning and statistics.

2. How Does the Accuracy Formula Work?

The calculator uses the formula:

\[ \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} \]

Where:

\( TP \) — True Positives: Correctly identified positive cases
\( TN \) — True Negatives: Correctly identified negative cases
\( FP \) — False Positives: Incorrectly identified positive cases
\( FN \) — False Negatives: Incorrectly identified negative cases

Explanation: Accuracy is calculated by dividing the number of correct predictions by the total number of predictions.

3. Importance of Accuracy in Classification

Details: While accuracy is a fundamental metric, it may not tell the whole story, especially with imbalanced datasets. It's most useful when the classes are roughly equally distributed.

4. Using the Calculator

Tips: Enter the counts for true positives, true negatives, false positives, and false negatives from your confusion matrix. All values must be ≥ 0.

5. Frequently Asked Questions (FAQ)

Q1: What is a good accuracy score?
A: It depends on the context. For binary classification with balanced classes, >80% is often good, but compare against a baseline model.

Q2: When shouldn't I use accuracy?
A: Accuracy can be misleading with imbalanced datasets (e.g., 95% negative cases). Consider precision, recall, or F1 score instead.

Q3: Can accuracy be greater than 1?
A: No, accuracy ranges from 0 (worst) to 1 (best), representing the proportion of correct predictions.

Q4: What's the difference between accuracy and precision?
A: Accuracy measures overall correctness, while precision measures how many selected items are relevant (TP/(TP+FP)).

Q5: How do I get these values from my model?
A: Most machine learning libraries can generate a confusion matrix that provides TP, TN, FP, FN counts.