# Optimization of hyperparameters for Discover Best Model (Continuous Response)

###### Note

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 R2.

## R-squared

R2 is the percentage of variation in the response that the model explains.

### Interpretation

Use R2 to determine how well the model fits your data. The higher the R2 value, the better the model fits your data. R2 is always between 0% and 100%.

You can graphically illustrate the meaning of different R2 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 R2, 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 R2.

### Interpretation

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

## Predictor count for node splitting

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

## Minimum internal node size

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

## Number of bootstrap samples

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