Logistic Regression Analytics View
Builds a logistic regression model to predict binary Target Variable column value from Predictor Variable(s) column values.
Input data should contain following columns.
- Target Variable - Column that has binary values to be predicted by the logistic regression model.
- Predictor Variable(s) - Column(s) that has values on which the prediction by logistic regression model is based.
- Sample Data Size - Number of rows to sample before building logistic regression model.
- 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.
- P Value Threshold to be Significant - P value must be smaller than this value for coefficients to be considered statistically significant.
- Sort Variables by Coefficients - If set to TRUE, variables displayed in Coefficients View are sorted by coefficients.
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 "Logistic Regression Analysis" for Type.
- Select Target Variable column.
- Select Predictor Variable(s) columns.
- Set Analytics Properties if necessary.
- Click Run button to run the analytics.
- Select view type by clicking view type link to see each type of generated visualization.