How to Run Machine Learning Diagnostics - Prediction-Accuracy Table

This article describes how to create a Machine Learning a Prediction-Accuracy Table, as shown below. This type of output is also referred to as a confusion matrix, classification accuracy, and hit-miss table.

Requirements

• A Machine Learning output (eg, CART, Support Vector Machine, Linear Discrimination Analysis, etc.)

Method 1 - creating a separate output 

1. Select the Machine Learning output.
2. Go to the object inspector > Inputs > DIAGNOSTICS > Prediction Accuracy Table, or go to the Anything menu > Advanced Analysis > Machine Learning > Diagnostics > Prediction Accuracy Table.
   

Method 2 - changing the output type

1. Select the Machine Learning output.
2. Go to the object inspector > Visualization > Regression and Machine Learning Diagnostics > Prediction Accuracy Table.  

See Also

How to Create a Classification And Regression Trees (CART)

How to Run Deep Learning

How to run Machine Learning Diagnostics - Table of Discriminant Function Coefficients extension

How to Create an Ensemble of Machine Learning Models

How to Run a Gradient Boosting Machine Learning Model

How to Run Machine Learning Linear Discriminant Analysis

How to Run Mixed-Mode Tree

How to Run Random Forest

How to Compare Machine Learning Models

How to Save Machine Learning Discrimination Variables

How to Save Machine Learning Predicted Values Variables

How to Save Machine Learning Probability of Each Response Variable

How to Run Support Vector Machine