Classification Metrics

Average Precision Score measures the accuracy of a model's predictions in classification tasks, balancing precision and recall.

Confusion Matrix Metrics evaluate classification model performance using key indicators like accuracy, precision, recall, and F1 score.

F-Measure is a metric used to evaluate the performance of classification models, balancing precision and recall.

F-Score is a statistical measure used to evaluate the accuracy of binary classification models.

Gini Impurity measures the impurity of a dataset, used primarily in decision tree algorithms to evaluate splits.

Hamming Loss measures the fraction of wrong labels in multi-label classification tasks.

Histogram Loss measures the discrepancy between predicted and actual distributions in classification tasks.

Log Loss measures the performance of a classification model where the output is a probability between 0 and 1.

Macro-Average calculates the overall performance of a model across multiple classes in classification tasks.

The misclassification rate measures the proportion of incorrect predictions made by a classification model.

Negative Predictive Value (NPV) measures the accuracy of a test in identifying negative cases.