Specify the default methods for
TreeNet^{®}
Regression.
The changes you make to the defaults remain until you change them again, even
after you exit Minitab.

- Loss function
- Choose the loss function to create your model. You can compare the
results from several functions to determine the best choice for your
application.
- Squared error: This is a mean-based loss function. This loss function works well across many applications.
- Absolute deviation: The absolute deviation function is a median-based loss function.
- Huber: The Huber function is a hybrid of the squared error and the absolute deviation function.

- Maximum terminal nodes per tree and Maximum tree depth
- You can also limit the size of the trees. Choose one of the following
to limit the size of the trees.
- Maximum terminal nodes per tree: Enter a value between 2 and 2000 to represent the maximum number of terminal nodes of a tree. Usually, 6 provides a good balance between calculation speed and the investigation of interactions among variables. A value of 2 eliminates the investigation of interactions.
- Maximum tree depth: Enter a value between 2 and 1000 to represent the maximum depth of a tree. The root node corresponds to a depth of 1. In many applications, depths from 4 to 6 give reasonably good models.

- Missing value penalty
- Enter a penalty value for a predictor with missing values. Because it is easier to be a good splitter with less data, predictors with missing data have an advantage over predictors without missing data. Use this option to penalize predictors with missing data.
- High level category penalty
- Enter a penalty value for categorical predictors that have many values. Because categorical predictors with many levels can distort a tree due to their increased splitting power, they have an advantage over predictors with less levels. Use this option to penalize predictors with many levels.