Decision Tree Analytics View
Builds a decision tree to predict Target Variable column value from Predictor Variable(s) column values.
Input data should contain following columns.
- Target Variable - Column that has values to be predicted by the decision tree. It can be of categorical (binary or multi-class) or nuneric value.
- Predictor Variable(s) - Column(s) that has values on which the prediction by decision tree is based.
- Sample Data Size - Number of rows to sample before building decision tree.
- Max # of Categories for Target Variable - If categorical Target Variable column has more categories than this number, less frequent categories are combined into 'Other' category.
- Max # of Categories for Predictor Vars - If categorical predictor column has more categories than this number, less frequent categories are combined into 'Other' category.
- Adjust Imbalanced Data - Adjust imbalance of data in Target Variable (e.g. FALSE being majority and TRUE being minority.) by SMOTE (Synthetic Minority Over-sampling Technique) altorithm.
- Random Seed - Seed used to generate random numbers. Specify this value to always reproduce the same result.
How to Use This Feature
- Click Analytics View tab.
- If necessary, click "+" button on the left of existing Analytics tabs, to create a new Analytics.
- Select "Decision Tree" for Type.
- Select Target Variable column that you want to predict with the decision tree.
- Select Predictor Variable(s) columns to be the basis of the prediction by the decison tree.
- Click Run button to run the analytics.
- Select view type by clicking view type link to see each type of generated visualization.