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Available measure types
The model evaluation methods in WSO2 ML can be categorized into four types as follows.
Numerical predictions
These methods involve making a numerical prediction based on the dataset analyzed.
Binary classification
Binary classification involves classifying the data items in a dataset into two categories.
Terminology of Binary Classification Metrics
Binary Classification Metrics refer to the following two formulas used to calculated the reliability of a binary classification model.
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| Abbreviation | Expansion | Meaning |
|---|---|---|
| P | Positives | The total number of positive outcomes (i.e. the total number of items that actually belong to the positive class). |
| N | Negatives | The total number of negative items (i.e. the total number of items that actually belong to the negative class). |
| TP | True Positive | TP data items:
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| FP | False Positive | FP data items:
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| TN | True Negative | TN data items:
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| FN | False Negative | FN data items:
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Multi-class classification involves classifying the items in a dataset into multiple categories.
Clustering
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Model evaluation measures
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