Classification Metrics 편집하기

'''Classification metrics''' are evaluation measures used to assess the performance of classification models in machine learning and data science. These metrics help determine how well a model can predict the correct class labels, particularly in supervised learning tasks.
==Common Classification Metrics==
There are several widely used classification metrics, each serving different aspects of model performance:
*'''Accuracy''': Measures the ratio of correct predictions to the total predictions. Useful when the dataset is balanced.
*'''Precision''': Measures the ratio of true positive predictions to the sum of true positive and false positive predictions. Important when the cost of false positives is high.
*'''Recall''': Measures the ratio of true positive predictions to the sum of true positives and false negatives. Useful when the cost of false negatives is high.
*'''F1 Score''': The harmonic mean of precision and recall, providing a balance between the two. Suitable when both false positives and false negatives are critical to minimize.
==Advanced Classification Metrics==
In addition to basic metrics, there are more advanced metrics for evaluating models, especially in cases with multiple classes or imbalanced data:
*'''AUC-ROC Curve''': A graphical representation that plots the true positive rate against the false positive rate at various threshold settings. A higher Area Under the Curve (AUC) indicates better model performance.
*'''Logarithmic Loss (Log Loss)''': A metric that penalizes incorrect classifications with a high confidence score. Useful in probabilistic classification tasks.
*'''Cohen's Kappa''': A metric that accounts for agreement occurring by chance. Often used when there is a strong imbalance between classes.
*'''Matthews Correlation Coefficient (MCC)''': A balanced measure that takes into account true and false positives and negatives, providing a more reliable measure for imbalanced datasets.
==Importance of Choosing the Right Metric==
The choice of classification metric depends on the nature of the data and the specific goals of the model:
*Use accuracy for balanced datasets where overall correctness is essential.
*Use precision when false positives are costly, such as in fraud detection.
*Use recall when false negatives are costly, such as in medical diagnoses.
*Use F1 Score when both false positives and false negatives are equally important.
==Limitations==
Classification metrics may not capture all aspects of model performance and can be misleading if used inappropriately. For example:
*Accuracy may not be meaningful for imbalanced datasets.
*Precision or recall alone may not provide a complete picture of the model's effectiveness.
*Advanced metrics like AUC-ROC may be complex to interpret without understanding the underlying thresholds.
==See Also==
*[[Accuracy (Data Science)|Accuracy]]
*[[Precision (Data Science)|Precision]]
*[[Recall (Data Science)|Recall]]
*[[F1 Score]]
*[[Confusion Matrix]]
*[[AUC]]
*[[ROC Curve]]
[[Category:Data Science]]