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Classification models
Accuracy
"Accuracy" is simple merely the percentage of correctly classified samples.
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Balanced accuracy is the averaged accuracy for each class, e.g. (positive_class_accuracy + negative_class_accuracy) / 2.
This parameter is important for imbalanced datasets, which have significantly different number of samples in different classes.
Confusion matrix
Confusion matrix shows the number of samples from a particular class classified as another particular class.
For binary classification models with "yes" and "no" classes, the confusion matrix shows the number of:
- true positives
- true negatives
- false positives
- false negatives