The algorithm can handle numerical as well as categorical attributes. There is no requirement to binarize or standardize the data prior to tree building. The only limitation is for the number of values of the nominal attribute. Typically, this number cannot exceed 100.
Full specification of the model settings contains the elements of the General Algorithm Settings and Transformation Settings.
The class responsible for providing the algorithm settings specific to Classification Trees is SmartTreeSettings. See javadoc for the programming details of using it in Gython.
Table 31.1. SmartTreeSettings properties summary table
| Option | Description | Possible values | Default value |
|---|---|---|---|
| Aggregating method | Method used in scoring to assign weights to trees. Method1: validation data. Method2: 0.632*validation + 0.368*train. | Method1, Method2 | Method2 |
| Change usages on each split | True: select a random set of active variables on each split; false: active variable configuration is set once for a tree | true/false | true |
| Min Node Variability Prc | Variability of the minNodeSizePrc parameter. 0: inactive. This parameter is set once for a tree | Real numbers from the interval [0,100] | 5.0 |
| Multiple trees | How many bootstrapped classification tree models to create. | Integer numbers grater or equal than 1 | 100 |
| Number of attributes per each split | Number of active attributes to select for each split or each tree. -1: sqrt(number of active attr.) is used. | -1 or integer numbers greater then 1 | -1 |
| Positive | Positive target category for lift. Null: the first category will be used. | Category names from logicalData | null |
| Prob Aggregate | True: each tree outputs a probability value and the probabilities are averaged for the final result. False: each tree produces a decision and the final results is based on the proportions of the votes. | true/false | true |
| Quantiles | Number of lift quantiles; if <=0 then don't calculate | integer numbers | 0 |
| Save Trees | True: the built trees wil be saved and each tree can be viewed and applied. False: only importance and performance statistics will be saved and the model cannot be applied and tested. | true/false | true |
| Split Evaluation Variability | True: each tree will be built using a split evaluation method selected randomly from: entropy, gini, gainRatio. False: only one setting will be used. | true/false | true |
| Stratified Sampling | True: bootstrap samples from each target value separatelly. False: bootstrap samples from the entire training set. | true/false | true |
| Threads | Number of threads used to build the model. -1: use the maximum number of available threads. | integer numbers | 1 |
| Variable Sampling | Variable sampling algorithm. True: sample N variables from all active variables without replacement. False: each tree will take the consecutive N variables from the list without sampling. | true/false | true |
| Weight from valid err | True: during scoring each tree is assigned a weight based on its validation data error | true/false | true |
| Weighting Stat | Error statistic used to evaluate each model on out-of-bag data and weight it in scoring. | sse, acc, mae, continuous | mae |