Properties that can be configured for the R-CNR Tree algorithm.
| Property | Description |
|---|---|
| Output Mode | Select the mode in which you want to use the output of this
algorithm. Possible values:
|
| Features | Select the input columns with which you want to perform the analysis. |
| Target Variable | Select the target column for which you want to perform the analysis. |
| Missing Values | Select the method for handling missing values. Possible
methods:
|
| Algorithm Type | Select the type of analysis you want the algorithm to
perform. Possible values:
|
| Minimum Split | Enter the minimum number of observations required for splitting a node. The default value is 10. |
| Split Criteria | Select the splitting criteria of the node. Possible values:
|
| Predicted Column Name | Enter a name for the newly-created column that contains the predicted values. |
| Complexity Parameter | Enter the complexity parameter that saves computing time by preventing any split that does not improve the fit. The default value is 0.005. |
| Maximum Depth | Enter the maximum node level in the final tree with the root node
counted as level 0. Note If the maximum depth is greater than 30,
the algorithm does not produce results as expected (on 32-bit
machines).
|
| Cross Validation | Enter the number of cross validations. A higher cross validation value increases the computation time and produces more accurate results. |
| Prior Probability | Enter the vector of prior probabilities. |
| Use Surrogate | Select the surrogate to use in the splitting process. Possible
values:
|
| Surrogate Style | Enter the style that controls the selection of the best
surrogate. Possible values:
|
| Maximum Surrogate | Enter the maximum number of surrogates to be retained at each node in a tree. |
| Show Probability | Select the Show Probability check box to get the probability of predicted values during scoring of a classification model. |