{"id":63,"date":"2026-04-03T11:05:18","date_gmt":"2026-04-03T11:05:18","guid":{"rendered":"https:\/\/gigz.pk\/ml\/?post_type=lesson&p=63"},"modified":"2026-04-07T07:02:11","modified_gmt":"2026-04-07T07:02:11","slug":"model-evaluation-metrics","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/ml\/lesson\/model-evaluation-metrics\/","title":{"rendered":"Model Evaluation Metrics"},"content":{"rendered":"\n

Model evaluation metrics are used to measure how well a Machine Learning model performs on data. Choosing the right metric depends on the type of problem\u2014regression<\/strong> or classification<\/strong>. Evaluation metrics help understand accuracy, errors, and overall reliability of the model.<\/p>\n\n\n\n

Evaluation Metrics for Classification<\/h2>\n\n\n\n

Accuracy<\/h3>\n\n\n\n

Accuracy measures the percentage of correct predictions made by the model. It is calculated as:<\/p>\n\n\n\n

Accuracy = (Number of Correct Predictions) \/ (Total Predictions)<\/code><\/p>\n\n\n\n

Accuracy works well when classes are balanced but may be misleading for imbalanced datasets.<\/p>\n\n\n\n

Precision<\/h3>\n\n\n\n

Precision measures the proportion of positive predictions that are actually correct.<\/p>\n\n\n\n

Precision = True Positives \/ (True Positives + False Positives)<\/code><\/p>\n\n\n\n

It is useful when the cost of false positives is high.<\/p>\n\n\n\n

Recall (Sensitivity)<\/h3>\n\n\n\n

Recall measures the proportion of actual positives correctly identified by the model.<\/p>\n\n\n\n

Recall = True Positives \/ (True Positives + False Negatives)<\/code><\/p>\n\n\n\n

It is important when the cost of missing positive cases is high, such as in medical diagnosis.<\/p>\n\n\n\n

F1 Score<\/h3>\n\n\n\n

The F1 Score is the harmonic mean of precision and recall. It balances both metrics and is useful for imbalanced datasets.<\/p>\n\n\n\n

F1 Score = 2 * (Precision * Recall) \/ (Precision + Recall)<\/code><\/p>\n\n\n\n

ROC-AUC<\/h3>\n\n\n\n

The ROC-AUC (Receiver Operating Characteristic \u2013 Area Under Curve) evaluates the model\u2019s ability to distinguish between classes. A higher AUC indicates better classification performance.<\/p>\n\n\n\n

Evaluation Metrics for Regression<\/h2>\n\n\n\n

Mean Absolute Error (MAE)<\/h3>\n\n\n\n

MAE measures the average absolute difference between predicted and actual values.<\/p>\n\n\n\n

MAE = Sum(|Predicted - Actual|) \/ Number of Observations<\/code><\/p>\n\n\n\n

It gives an easy-to-understand measure of average error.<\/p>\n\n\n\n

Mean Squared Error (MSE)<\/h3>\n\n\n\n

MSE measures the average squared difference between predicted and actual values.<\/p>\n\n\n\n

MSE = Sum((Predicted - Actual)\u00b2) \/ Number of Observations<\/code><\/p>\n\n\n\n

It penalizes larger errors more than MAE.<\/p>\n\n\n\n

Root Mean Squared Error (RMSE)<\/h3>\n\n\n\n

RMSE is the square root of MSE and provides error in the same units as the target variable.<\/p>\n\n\n\n

R\u00b2 Score (Coefficient of Determination)<\/h3>\n\n\n\n

R\u00b2 measures how well the model explains the variance in the data. A score closer to 1 indicates better performance.<\/p>\n\n\n\n

Conclusion<\/h2>\n\n\n\n

Model evaluation metrics are essential for understanding and improving Machine Learning models. Classification and regression problems require different metrics. Using the right metrics helps identify strengths, weaknesses, and areas for improvement, leading to more accurate and reliable models.

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