# Confusion matrix

A **Confusion Matrix** can be used to evaluate the performance of a model when predicting discrete variables (classification).

##### NOTE

A classification model is simply a model which is used to predict a discrete variable.

There are cells in the matrix for each combination of actual vs. predicted values. Each cell displays a count, which is the number of times the predicted value matched the actual value.

By changing the **Display Value** a probability can be displayed instead.

## Probability given actual

Probability given actual is the cell count divided by the row count (total number of that actual value in the data).

##### NOTE

For binary classifiers, the terms recall rate/sensitivity/true positive rate/specificity/true negative rate are sometimes used to refer to particular cells.

## Probability given predicted

Probability given predicted is the cell count divided by the column count (total number of that predicted value).

##### NOTE

For binary classifiers, the terms precision/positive predicted value, are sometimes used to refer to particular cells.

An example confusion matrix is shown below.

##### NOTE

The cells that lie on the diagonal from top left to bottom right, represent correct predictions, while off diagonal are incorrect predictions. The diagonal elements are surrounded by a black border for easy identification.

## Grid & Error tabs

In addition to the matrix view, you can view a detailed list of all the cell entries on the **Grid** tab and all but the correct entries on the **Errors** tab.

The list views are useful as they can be quickly ordered by *Count*, *Probability*, *Probability | Actual* or *Probability | Predicted* in order to quickly identify potential problems (or successes).

## User Interface

A confusion matrix can be generated from the **Statistics** tab in the Batch Query window.

When using the Data Map to map data to variables in the model, you can either:

Ensure the variable you are predicting is not mapped to data and ensure the actual value is available by adding it as an

**Information Column**in the Data map window.Leave the mapping between the data and the variable you are predicting, and use retracted evidence so that the prediction does not use the actual value.