Accuracy (Data Science)

IT 위키

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.

1 Definition[편집 | 원본 편집]

Accuracy is calculated as:

Accuracy = (True Positives + True Negatives) / (Total Number of Instances)

This metric is often used in classification problems, where the goal is to determine how well a model can predict class labels.

2 Importance of Accuracy[편집 | 원본 편집]

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.

3 When to Use Accuracy[편집 | 원본 편집]

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

4 Limitations of Accuracy[편집 | 원본 편집]

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

5 Alternative Metrics[편집 | 원본 편집]

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.

6 Conclusion[편집 | 원본 편집]

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.

7 See Also[편집 | 원본 편집]