You can access your results directly by opening the output dataset or depending on your business needs, consume the output dataset in a BI story. If the application dataset contains more columns than your training dataset, the additional columns will be ignored by the application process.
Apply predictive model
Open the relevant predictive model and select theApply Predictive Model icon, opening the apply model dialog.
Apply to population
In the apply to population section, select the new dataset (application dataset) that you want to apply your predictive model on in the Data Source field.
Generated dataset
In this section, you have a number of options to select the additional columns you want to include in your output dataset.
Replicated columns: Select the variables from the dataset that you used to train the model that should be part of the output dataset. The application process does not take into account any columns of the application dataset that do not belong to the training dataset.
Statistics & Predictions: In the statistics and predictions dropdown, the following data can be selected to be included in the output dataset. If you do not select any statistics or predictions, only the target variable and the key variable(s) are included.
The Statistics & Predictions options include:
- Apply Date: It's the start date of the predictive model application. The type of the column is TIMESTAMP.
- Train Date: It's the start date of the predictive model training. The type of the column is TIMESTAMP.
- Assigned Bin: During the application step, Smart Predict refers to the bins defined in the training step to assign the current observations from the input dataset to the relevant bin. It compares each value obtained by the predictive model with the limits of each assigned bin defined in the training step, then it assigns each observation to the relevant bin.
See the interaction below to find out more about assigned bins in the training and applying phases.
- Outlier Indicator: For each row in the application dataset, the Outlier Indicator is 1 if the row is an outlier with respect to the target, otherwise it is 0. An observation is considered an outlier when the prediction error is greater than 3 times the average prediction error found on similar observations.
- Predicted Category: For each row in the application dataset, the Predicted Category is the target category determined by the predictive model. Classification predictive models use nominal target with 2 values only.
- Predicted Probability: Classification predictive models use nominal target with 2 values only. For each row in the application dataset, the Prediction Probability is the probability that the Predicted Category is the target value.
Output As: Give a name to your generated dataset.
