Specify the default methods for
The changes you make to the defaults remain until you change them again, even
after you exit Minitab.
Choose the loss function to create your model. You can compare the
results from several functions to determine the best choice for your
This is a mean-based loss function. This loss function works well across many
The absolute deviation function is a median-based loss function.
The Huber function is a hybrid of the squared error and the absolute deviation
function, specify a
The loss function starts as the squared error. The loss function remains the
squared error as long as the value is less than the switching value. If the
squared error exceeds the switching value, then the loss function becomes the
absolute deviation. If the absolute deviation becomes less than the switching
value, then the loss functions becomes the squared error again
terminal nodes per tree
You can also limit the size of the trees. Choose one of the following
to limit the size of the trees.
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.
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.
0.0 ≤ K ≤ 2.0, for example:
K = 0: Specifies no
K = 2: Specifies
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.