Debriefing Regression Predictive Model Results

A predictive model produces performance indicators and reports as a result of a successful training. Here is a short summary of the different components that you can use to debrief your results so you can verify the accuracy of your predictive model.

The Prediction Confidence indicates the capacity of your predictive model to achieve the same degree of accuracy when you apply it to a new dataset, which has the same characteristics as the training dataset. If the Prediction Confidence is greater than or equal to 95%, it is considered as a good score. If it is less than 95%, then you must improve your predictive model, adding new rows to your dataset, for example.
Root Mean Squared Error (RMSE) measures the average difference between values predicted by your predictive model and the actual values. The smaller the RMSE value, the more accurate the predictive model is.
How does the target value appear in the different datasets? Get some descriptive statistics on the target value per dataset. For more information, refer to Target Statistics.
Which influencers have the highest impact on the target? Check how the top five influencers impact on the target. For more information, refer to Influencer Contributions.
Which group of categories has the most influence on the target? In Influencer Contributions, you can analyze the influence of different categories of an influencer on the target: If the influence value is positive, we are more likely to get "minority value". If the influence value is negative we are less likely to get "minority value". For more information, refer to Category Influence, Grouped Category Influence and Grouped Category Statistics.
Can I see any errors in my predictive model? Is my predictive model producing accurate predictions? Compare the prediction accuracy of your predictive model to a perfect predictive model using a graph and detect the model errors very quickly. For more information, refer to Predicted vs. Actual.

What's next?

If you are satisfied with the results of your predictive model, use it. For more information, see Generating and Saving the Predictions for a Classification or Regression Predictive Model.

If you are not satisfied, try to improve your predictive model changing the settings, for example.