决策树工具

Model Type: The type of model used to predict the target variable.

在模型估计中使用样本重：这个选项允许您选择一个字段，该字段在创建模型估计时对每条记录进行加权。

如果字段同时用作预测因子和样本权重，则输出权重变量字段名称前面将加上 Right_。

The splitting criteria when using a Regression model is always Least Squares.

Use surrogates to: Select the method for using surrogates in the splitting process. Surrogates are variables related to the primary variable that are used to determine the split outcome for a record with missing information.

Select best surrogate split using: Select the criteria for choosing the best variable to split on from a set of possible variables.

The minimum number of records needed to allow for a split: Set the number of records that must exist before a split occurs. If there are fewer records than the minimum number, then no further splits are allowed.

The allowed minimum number of records in a terminal node: Set the number of records that can be in a terminal node. A lower number increases the potential number of final terminal nodes at the end of the tree.

The number of folds to use in the cross-validation to prune the tree: Set the number of groups (N) the data should be divided into when testing the model. The number defaults to 10, but other common values are 5 and 20. A higher number of folds gives more accuracy to the tree but may take longer to process. When the tree is pruned by using a complexity parameter, cross-validation determines how many splits, or branches, are in the tree. In cross validation, N - 1 of the folds are used to create a model, and the other fold is used as a sample to determine the number of branches that best fits the holdout fold in order to avoid overfitting.

The maximum allowed depth of any node in the final tree: Set the number of levels of branches allowed from the root node to the most distant node from the root to limit the overall size of the tree.

The maximum number of bins to use for each numeric variable: Enter the number of bins to use for each variable. By default, the value is calculated based on the minimum number of records needed to allow for a split.

Set complexity parameter: A value that controls the size of the decision tree. A smaller value results in more branches in the tree, and a larger value results in fewer branches. If a complexity parameter is not selected, the parameter is determined based on cross-validation.

Decomposetree into rule-based model: Change the structure of the output algorithm from a decision tree into a collection of unordered, simple if-then rules. Select Threshold number of bands to group rules into to Select a number of bands to group rules into where the number set is the band threshold.

Model should evaluate groups of discrete predictors for splits: Group categorical predictor variables together. Select to reduce overfitting when there are important discrete attributes that have more than four or five values.

Use predictor winnowing (i.e. feature selection): Select to simplify the model by attempting to exclude non-useful predictors.

Prune tree: Select to simplify the tree to reduce overfitting by removing tree splits.

Evaluate advanced splits in the data: Select to perform evaluations with secondary variables to confirm what branch is the most accurate prediction.

Use stopping method for boosting: Select to evaluate if boosting iterations are becoming ineffective and, if so, stop boosting.

Select number of boosting iterations: Select a 1 to use a single model.

Select confidence factor: This is the analog of rpart’s complexity parameter.

Select number of samples that must be in at least 2 splits: A larger number gives a smaller, more simplified, tree.

Percent of data held from training for model evaluation: Select the portion of the data used to train the model. Use the default value 0 to use all of the data to train the model. Select a larger value to hold that percent of data from training and evaluation of model accuracy

Select random seed for algorithm: Select the value of the seed. 时间戳必须是正整数。

较低的分辨创建相对较小的文件，最适合在显示器上查看。

更高的分辨率可以创建一个更大的文件，具有更好的打印质量。