Analyzing the Results of Your Regression Predictive Model

Once you've trained your regression predictive model, you can analyze its performance to make sure it's as accurate as possible.

Use the dropdown list to access and analyze the reports on influencers and predictive model performance.

Click the area for more information.

What do the values of the two main performance indicators mean?

Quickly check if your predictive model is accurate and robust, checking the two main performance indicators:

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.
Prediction Confidence indicates the capacity of your predictive model to achieve the same degree of accuracy when you apply it to a new data source, which has the same characteristics as the training data source. It takes a value between 0% and 100%. This value should be as close as possible to 100%. To improve your Prediction Confidence, you can add new rows to your data source, for example.

For more information, refer to Root Mean Squared Error (RMSE) and Prediction Confidence.

How does the target value appear in the different data sources?

Get some descriptive statistics on the target value per data source.

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. Only the top five contributing influencers are displayed as a default.

For more information, refer to Influencer Contributions.

Which group of categories has the most influence on the target?

In the Influencer Contributions report, 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".

The influence of a category can be positive or negative.

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 predictive model errors very quickly.

For more information, refer to Predicted vs. Actual.

What's next?

You have two possibilities:

Your are satisfied with your predictive model's performance. Then you can use it: Generating Your Predictions.
You would like to see if you can improve your predictive model's performance:
- Duplicate your current predictive model and experiment with updated settings. You can then compare the two versions and find the best one. See Duplicating a Predictive Model.
- Update the settings of your predictive model and retrain it. See Define Settings and Train a Classification or Regression Predictive Model.
  Caution
  You will erase the previous version!
- Delete your predictive model. See Deleting a Predictive Model.