Steps involved in calculating predictor importance

Chaitra

Hi all,

Could anyone let me know the steps involved in Steps involved in calculating predictor importance?

varunm1

Hello @Chaitra

Are you asking about "Explain Predictions" operator that provides you with which are supporting and contradicting attributes for prediction? Or the automodel that provides you with the importance of each predictor by giving you three different color codings (Red, Yellow and green)?

If it is related to Explain predictions. It works based on the Pearson correlation between the attribute and prediction.  Below is the discussion we had regarding this recently.
https://community.rapidminer.com/discussion/55000/explain-predictions-table-coloring-explained

Calculate the correlation between the given attribute and the predictions.  Make sure that the
* the predictions are set in a one-vs-all fashion for multiclass problems.  It uses the confidence
* for the class to correlated with.
*
* For nominal attributes we just into 1 (same value as the one predicted) vs. 0 (different value).
*
* Please note that this method artificially sets the standard deviation to a small value in case
* of all labels being the same (which can happen if the model is really confident in certain
* areas). First, we artificially change one random label in case they are all the same.
* Then we also capture the case that the standard deviations are still 0 by replacing it
* by a small value then.
*
* These small changes will avoid that all correlations for all attributes would be NaN
* otherwise.  Because of those changes, this method should not be used for calculating
* regular correlations.

Telcontar120

Also some algorithms include information about predictor importance as part of their model output.  For example, you can look at the standardized coefficients for regression models, GBT has a variable importance table, Decision Trees use the most important attributes higher up on the tree, etc.