The default (if you didn’t go into model customization) is rpart. It will look different depending on which algorithm you selected to create your Decision Tree with in the tool’s configuration. R (Report): This is a static report that summarizes your Decision Tree Model. It can be used as an input for other Predictive Tools, like the Score Tool, which will run your model to estimate the target variable, or the Model Comparison Tool (available in the Predictive District of the Alteryx Gallery) which compares the performance of different models on a validation data set. It is the actual Decision Tree Model that you have created with the Decision Tree Tool. Classification Trees are typically evaluated with confusion matrices and F1-Scores, whereas Regression Trees are assessed with values like R 2 and Mean Square Error (MSE). This is because categorical and continuous predictions cannot be assessed using the same metrics. Classification and regression trees are very similar, but do differ on a few points: most notably how splits (variable thresholdson which the data are divided) are determined, but also on how the resulting model and predictions are assessed. Your target variable determines whether the tool constructs a Classification Tree or a Regression Tree. Like the configuration, the outputs of the Decision Tree Tool change based on (1) your target variable, which determines whether a Classification Tree or Regression Tree is built, and (2) which algorithm you selected to build the model with (rpart or C5.0). For a general description on how Decision Trees work, read Planting Seeds: An Introduction to Decision Trees, for a run-down on the configuration of the Decision Tree Tool, check out the Tool Mastery Article, and for a really awesome and accessible overview of the Decision Tree Tool, read the Data Science Blog Post: An Alteryx Newbie Takes on the Predictive Suite: Decision Tree. This article reviews the outputs of the Decision Tree Tool.
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