Editing Accuracy (Data Science)

Accuracy is a metric used in data science to measure the performance of a model, particularly in classification problems. It represents the ratio of correctly predicted instances to the total number of instances.
==Definition==
Accuracy is calculated as:
:'''<big>Accuracy = (True Positives + True Negatives) / (Total Number of Instances)</big>'''
This metric is often used in classification problems, where the goal is to determine how well a model can predict class labels.
==Importance of Accuracy==
Accuracy provides insights into the overall effectiveness of a model, but it has limitations, particularly in the context of imbalanced data. Despite its simplicity, accuracy is a fundamental starting point for evaluating model performance.
==When to Use Accuracy==
Accuracy is best suited for:
*Balanced datasets, where each class has a similar number of observations
*Initial model evaluation, providing a quick assessment of performance
==Limitations of Accuracy==
Accuracy may not always reflect the true performance of a model, especially when:
*The dataset is imbalanced (e.g., when one class significantly outweighs the other)
*The cost of false positives or false negatives is high
==Alternative Metrics==
In cases where accuracy may be misleading, consider the following alternative metrics:
*'''Precision''': Measures the ratio of true positives to the sum of true positives and false positives. Useful in cases where false positives are costly.
*'''Recall''': Measures the ratio of true positives to the sum of true positives and false negatives. Important when capturing all positive cases is critical.
*'''F1 Score''': Combines precision and recall into a single metric. Useful when both false positives and false negatives are important to minimize.
==Conclusion==
While accuracy is a popular metric, it is essential to consider the data context and explore alternative metrics if the dataset is imbalanced or if there are specific costs associated with incorrect classifications.
==See Also==
*[[Precision]]
*[[Recall]]
*[[F1 Score]]
*[[Confusion Matrix]]
*[[Classification Metrics]]