Analyzing data
In the process of data analysis, you define a role for each predictor based on their predictive power and analyze them based on the known behavior of cases. Prediction Studio automatically prepares and analyzes every field (excluding outcome and weight) with two possible treatments for each field.
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Continuous or ordinal data
- Continuous treatment for numeric fields involves the use of values as predictions.
- Ordinal treatment of symbolic fields involves the use of the order of values as predictions.
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Categorical data
- Categorical treatment for numeric and symbolic fields involves using the probability of positive behavior for each (grouping of numeric predictors) as prediction.
- Ordinal symbolic and categorical symbolic or numeric go through a process of assigning the values to bins combining the bins to create a powerful, reliable relationship between bins and behavior.
- Defining the predictor role
Select the appropriate role for each predictor. A predictor is a field that has a predictive relationship with the outcome (the field whose behavior you want to predict).
- Analyzing and configuring predictors
Review the data and confirm the initial treatment of predictors.
- Outcome inferencing
Outcome inferencing allows you to analyze and handle unknown behavior captured in the data. Because of the unknown behavior, outcome inferencing and final data analysis steps are added in the process of data analysis.
- Virtual fields
Virtual fields allow you to create fields based on the ones that are available in the set of input fields known as data dictionary. Any virtual field becomes a part of the model that uses it.
- Generating data analysis reports
Generate data, behavior, and population reports when you develop models in Prediction Studio.
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