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

Find definitions and interpretation guidance for the Optimization
of Hyperparameters table.

After the creation of a model with
Discover Best Model
(Continuous
Response),
you can click
Select an Alternative Model
to explore other models. If you select a Random Forests
^{®} model, one option is to specify hyperparameters to fit new
models. If you specify hyperparameters, then the results include the
Optimization of Hyperparameters table. The table compares the combinations of
hyperparameters. The results that follow the Optimization of Hyperparameters
table are for the model with the best value of the optimality criterion, such
as the maximum R^{2}.

R^{2} is the percentage of variation in the response that the model
explains.

Use R^{2} to determine how well the model fits your data. The
higher the R^{2} value, the better the model fits your data.
R^{2} is always between 0% and 100%.

You can graphically illustrate the meaning of different R^{2}
values. The first plot illustrates a simple regression model that explains
85.5% of the variation in the response. The second plot illustrates a model
that explains 22.6% of the variation in the response. The more variation that
is explained by the model, the closer the data points fall to the fitted
values. Theoretically, if a model can explain 100% of the variation, the fitted
values would always equal the observed values and all of the data points would
fall on the line y = x.
###### Note

Because Random Forests® use out-of-bag data to calculate
R^{2}, but not to fit the model, overfitting of the model is not a
concern.

The mean absolute deviation (MAD) expresses accuracy in the same units as
the data, which helps conceptualize the amount of error. Outliers have less of
an effect on MAD than on R^{2}.

Use to compare the fits of different models. Smaller values indicate a better fit.

This row indicates the choice for the number of predictors to consider.

The minimum internal node size indicates the minimum number of cases a node can have and still split into more nodes.

The number of bootstrap samples indicates the number of trees in the analysis.