Metrics | XGBoosting

Helpful examples for evaluating XGBoost models using different performance metrics.

Performance metrics are quantitative measures used to evaluate the effectiveness and accuracy of a machine learning model, indicating how well the model’s predictions match the actual outcomes and guiding the selection and tuning of models.

Examples	Tags
Evaluate XGBoost Performance with Precision-Recall Curve	Evaluate Metrics Plot
Evaluate XGBoost Performance with ROC Curve	Evaluate Metrics Plot
Evaluate XGBoost Performance with the Accuracy Metric	Evaluate Metrics
Evaluate XGBoost Performance with the Classificaiton Error Metric	Evaluate Metrics
Evaluate XGBoost Performance with the Confusion Matrix	Evaluate Metrics Plot
Evaluate XGBoost Performance with the F1 Score Metric	Evaluate Metrics
Evaluate XGBoost Performance with the Log Loss Metric	Evaluate Metrics
Evaluate XGBoost Performance with the Mean Absolute Error Metric	Evaluate Metrics
Evaluate XGBoost Performance with the Mean Squared Error Metric	Evaluate Metrics
Evaluate XGBoost Performance with the Precision Metric	Evaluate Metrics
Evaluate XGBoost Performance with the Recall Metric	Evaluate Metrics
Evaluate XGBoost Performance with the ROC AUC Metric	Evaluate Metrics
Evaluate XGBoost Performance with the Root Mean Squared Error Metric	Evaluate Metrics