Specify the default settings for TreeNet® Regression

File > Options > Predictive Analytics > TreeNet® Regression

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.
With the Huber function, specify a Switching value. 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
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.
0.0 ≤ K ≤ 2.0, for example:
  • K = 0: Specifies no penalty.
  • K = 2: Specifies highest penalty.
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.
0.0 ≤ K ≤ 5.0, for example:
  • K = 0: Specifies no penalty.
  • K = 5: Specifies highest penalty.
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