Percent of error statistics due to largest residuals for Random Forests® Regression

Note

This command is available with the Predictive Analytics Module. Click here for more information about how to activate the module.

Use the percent of error statistics to examine the amount of error in the model fits from the worst fits.

Each row of the table shows the error statistics for the given percentage of residuals. The percent of the Mean Squared Error (MSE) that comes from the largest residuals is usually higher than the percent for the other two statistics. MSE uses the squares of the errors in the calculations, so the most extreme observations typically have the greatest influence on the statistic.

If you select validation with a test set in addition to out-of-bag validation, then the table displays results for both the out-of-bag data and the test set data.

A possible pattern is that a small percentage of the residuals account for a large portion of the error in the data. For example, in the following table, the total size of the data set is about 2930. From the perspective of the MSE, that indicates that 1% of the data account for about 36% of the error. In such a case, the 30 cases that contribute most of the error to the model can represent the most natural opportunity to improve the model. Finding a way to improve the fits for those cases leads to a relatively large increase in the overall performance of the model.

This condition can also indicate that you can have greater confidence in nodes of the model that do not have cases with the largest errors. Because most of the error comes from a small number of cases, the fits for the other cases are relatively more accurate.

Random Forests® Regression: Sale Price vs Lot Frontage, Lot Area, ...

Percent of Error Statistics Due to Largest Residuals % of Largest Out-of-Bag Residuals Count % MSE % MAD % MAPE 1.0 30 36.3855 9.5840 13.0409 2.0 59 46.9434 14.8347 18.0932 2.5 74 50.3622 16.9953 20.2317 3.0 88 53.1701 18.8880 22.0186 4.0 118 58.0879 22.5527 25.4151 5.0 147 62.0425 25.7845 28.3840 7.5 220 69.7824 32.9504 34.8161 10.0 293 75.0273 38.8507 40.2386 15.0 440 82.2816 48.6881 49.2733 20.0 586 86.9557 56.5610 56.7304
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