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These methods involve making a numerical prediction based on the dataset analysed. The available measures of this type are as follows.

analyzed.

Binary classification involves classifying the data items in a dataset into two categories.

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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: actually belong to the positive class are correctly included in the positive class
FP	False Positive	FP data items: actually belong to the negative class are incorrectly included in the positive class
TN	True Negative	TN data items: actually belong to the negative class are correctly included in the negative class
FN	False Negative	FN data items: actually belong to the positive class are incorrectly included in the negative class

The available measures of this type are as follows.

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Multi-class classification involves classifying the items in a dataset into multiple categories. The available measures of this type are as follows.

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This involves clustering the items in a dataset.

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The following methods are used to evaluate the performance of models in terms of accuracy.

Measure	Available for
Confusion Matrix	Binary classification models Multi-class classification models
Accuracy	Binary classification models Multi-class classification models
ROC Curve	Binary classification models
AUC	Binary classification models
Feature Importance	Binary classification Numerical prediction
Predicted vs Actual	Binary classification models Multi-class classification models
MSE	Numerical prediction
Residual Plot	Numerical Prediction

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