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	evaluation metrics in machine learning, classification metrics, regression evaluation metrics, precision recall f1 score, roc auc explained, mean squared error, choosing evaluation metrics, model performance measures		evaluation metrics in machine learning, classification metrics, regression evaluation metrics, precision recall f1 score, roc auc explained, mean squared error, choosing evaluation metrics, model performance measures
			[[Category:Artificial Intelligence]]

Thakshashila: Created page with "= Evaluation Metrics = '''Evaluation Metrics''' are quantitative measures used to assess the performance of machine learning models. Choosing the right metric is essential for understanding how well a model performs, especially in classification and regression problems. == Why Are Evaluation Metrics Important? == * Provide objective criteria to compare different models. * Help detect issues like overfitting or underfitting. * Guide model improvement and selection. * R..."

2025-06-10T05:46:52Z

Created page with "= Evaluation Metrics = '''Evaluation Metrics''' are quantitative measures used to assess the performance of machine learning models. Choosing the right metric is essential for understanding how well a model performs, especially in classification and regression problems. == Why Are Evaluation Metrics Important? == * Provide objective criteria to compare different models. * Help detect issues like overfitting or underfitting. * Guide model improvement and selection. * R..."

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= Evaluation Metrics =

'''Evaluation Metrics''' are quantitative measures used to assess the performance of machine learning models. Choosing the right metric is essential for understanding how well a model performs, especially in classification and regression problems.

== Why Are Evaluation Metrics Important? ==

* Provide objective criteria to compare different models.
* Help detect issues like overfitting or underfitting.
* Guide model improvement and selection.
* Reflect the business or real-world importance of model predictions.

== Types of Evaluation Metrics ==

=== 1. Classification Metrics ===

These metrics evaluate models that predict discrete categories (classes).

* '''Accuracy:''' Proportion of correct predictions over total predictions.
:<math> \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} </math>

* '''Precision:''' Proportion of true positives among all predicted positives.
:<math> \text{Precision} = \frac{TP}{TP + FP} </math>

* '''Recall (Sensitivity):''' Proportion of true positives among all actual positives.
:<math> \text{Recall} = \frac{TP}{TP + FN} </math>

* '''F1 Score:''' Harmonic mean of precision and recall, balancing both.
:<math> F1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} </math>

* '''Specificity:''' Proportion of true negatives among all actual negatives.
:<math> \text{Specificity} = \frac{TN}{TN + FP} </math>

* '''ROC AUC:''' Area under the Receiver Operating Characteristic curve, measuring true positive rate vs false positive rate across thresholds.

* '''Precision-Recall AUC (AUPRC):''' Area under the Precision-Recall curve, especially useful for imbalanced data.

=== 2. Regression Metrics ===

Used for models predicting continuous values.

* '''Mean Absolute Error (MAE):''' Average absolute difference between predicted and actual values.
:<math> MAE = \frac{1}{n} \sum_{i=1}^n | y_i - \hat{y}_i | </math>

* '''Mean Squared Error (MSE):''' Average squared difference, penalizes larger errors more.
:<math> MSE = \frac{1}{n} \sum_{i=1}^n ( y_i - \hat{y}_i )^2 </math>

* '''Root Mean Squared Error (RMSE):''' Square root of MSE, in same units as output.
:<math> RMSE = \sqrt{MSE} </math>

* '''R-squared (R²):''' Proportion of variance explained by the model.
:<math> R^2 = 1 - \frac{\sum (y_i - \hat{y}_i)^2}{\sum (y_i - \bar{y})^2} </math>

== Choosing the Right Metric ==

* '''Imbalanced Classification:''' Use Precision, Recall, F1 Score, or AUPRC instead of accuracy.
* '''Cost-Sensitive Tasks:''' Consider metrics that weigh errors differently.
* '''Regression:''' Use MAE or RMSE based on error tolerance.

== Related Pages ==

* [[Confusion Matrix]]
* [[Precision]]
* [[Recall]]
* [[F1 Score]]
* [[ROC Curve]]
* [[AUC Score]]
* [[Imbalanced Data]]
* [[Cost-Sensitive Learning]]

== SEO Keywords ==

evaluation metrics in machine learning, classification metrics, regression evaluation metrics, precision recall f1 score, roc auc explained, mean squared error, choosing evaluation metrics, model performance measures

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