UPCOMING WEBINAR: Faster (and better) survey analysis This article describes how to create a Machine Learning 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.)
Please note these steps require a Displayr license.
Method 1 - Creating a separate output
- Select the Machine Learning output.
- Go to the object inspector > Data > Diagnostics > Prediction Accuracy Table.
Method 2 - Changing the output type
- Select the Machine Learning output.
- Go to the object inspector > Visualization > Regression and Machine Learning Diagnostics > Prediction Accuracy Table.
Next
How to Create a Classification And Regression Trees (CART)
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 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