This article describes how to create a Random Forest output as shown below.
The table below shows the variable importance as computed by a Random Forest. The column called MeanDecreaseAccuracy contains a measure of the extent to which a variable improves the accuracy of the forest in predicting the classification.
Requirements
- Familiarity with the Structure and Value Attributes of Variable Sets.
- A numeric or categorical variable to be used as an Outcome variable to be predicted. When using a numeric variable a forest of regression trees is estimated; when using a categorical variable a forest of classification trees is estimated.
- Predictors variables will be considered as predictors of the outcome variable.
Method
- In the Anything menu select Advanced Analysis > Machine Learning > Random Forest.
- In the object inspector go to the Inputs tab.
- In the Output menu select the variable to be predicted by the predictor variables.
- Select the predictor variable(s) from the Predictor(s) list.
- OPTIONAL: Select the desired Output type:
- Importance: Produces importance tables, as illustrated above.
- Detail: This returns the default output from randomForest in the randomForest package. It includes a confusion matrix for classification trees, and the percentage of variance explained for regression trees.
- Prediction-Accuracy Table: Produces a table relating the observed and predicted outcome. Also known as a confusion matrix.
- OPTIONAL: Select the desired Missing Data treatment. (See Missing Data Options).
- OPTIONAL: Select Sort by Importance to sort the rows by importance (the last column in the table).
See Also
How to Create a Classification And Regression Trees (CART)
How to Run Machine Learning Diagnostics - Prediction-Accuracy Table
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 Compare Machine Learning Models
How to Run Machine Learning Linear Discriminant Analysis
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