The following options appear on the Bagging, Boosting, and Random Trees Data dialogs.

Ensemble Methods Data Dialog

Variables In Input Data

The variables included in the dataset appear here.

Selected Variables

Add continuous variables to be included in the model here.

Categorical Variables

Add categorical variables to be included in the model here.

Output Variable

The dependent variable or the variable to be classified appears here.

Number of Classes

Displays the number of classes in the Output variable.

Success Class

This option is selected by default. Select the class to be considered a "success" or the significant class in the Lift Chart. This option is enabled when the number of classes in the output variable is equal to 2.

Success Probability Cutoff

Enter a value between 0 and 1 here to denote the cutoff probability for success. If the calculated probability for success for an observation is greater than or equal to this value, than a "success" (or a 1) will be predicted for that observation. If the calculated probability for success for an observation is less than this value, then a "non-success" (or a 0) will be predicted for that observation. The default value is 0.5. This option is only enabled when the # of classes is equal to 2.

Boosting Parameters Dialog

Please see below for options appearing on the Boosting Parameters dialog.

Partition Data

Analytic Solver Data Mining includes the ability to partition a dataset from within a classification or prediction method by clicking Partition Data on the Parameters dialog. Analytic Solver Data Mining will partition your dataset (according to the partition options you set) immediately before running the classification method. If partitioning has already occurred on the dataset, this option will be disabled. For more information on partitioning, please see the Data Mining Partitioning chapter.

Rescale Data

Click Rescale Data to open the Rescaling dialog.

Use Rescaling to normalize one or more features in your data during the data preprocessing stage. Analytic Solver Data Mining provides the following methods for feature scaling: Standardization, Normalization, Adjusted Normalization and Unit Norm. For more information on this new feature, see the Rescale Continuous Data section within the Transform Continuous Data chapter that occurs earlier in this guide.

Number of Weak Learners

This option controls the number of "weak" classification models that will be created. The ensemble method will stop when the number or classification models created reaches the value set for this option. The algorithm will then compute the weighted sum of votes for each class and assign the "winning" classification to each record.

Weak Learner

Under Ensemble: Classification click the down arrow beneath Weak Leaner to select one of the six featured classifiers: Discriminant Analysis, Logistic Regression, k-NN, Naïve Bayes, Neural Networks, or Decision Trees. After a weak learner is chosen, the command button to the right will be enabled. Click this command button to control various option settings for the weak leaner.

AdaBoost Variant

The difference in the algorithms is the way in which the weights assigned to each observation or record are updated. (Please refer to the section Ensemble Methods in the Introduction to the chapter.)

In AdaBoost.M1 (Freund), the constant is calculated as:

In AdaBoost.M1 (Breiman), the constant is calculated as:

Adaboost (Breimanm) Constant

In SAMME, the constant is calculated as:

Samme Constant

(When the number of categories is equal to 2, SAMME behaves the same as AdaBoost Breiman.)

Random Seed for Resampling

If an integer value appears for Random Seed for Resampling, Analytic Solver Data Mining will use this value to specify the seed for random resampling of the training data for each weak learner. Setting the random number seed to a nonzero value (any number of your choice is OK) ensures that the same sequence of random numbers is used each time the dataset is chosen for the classifier. The default value is “12345”. If left blank, the random number generator is initialized from the system clock, so the sequence of random numbers will be different in each calculation. If you need the results from successive runs of the algorithm to another to be strictly comparable, you should set the seed. To do this, type the desired number you want into the box. This option accepts both positive and negative integers with up to 9 digits.

Show Weak Learner

To display the weak learner models in the output, select Show Weak Learner Models.

Bagging Parameters Dialog

Please see below for options that are unique to the Bagging Parameters dialog. For remaining option explanations, please see above.

Random Seed for Bootstrapping

If an integer value appears for Bootstrapping Random seed, Analytic Solver Data Mining will use this value to set the bootstrapping random number seed. Setting the random number seed to a nonzero value (any number of your choice is OK) ensures that the same sequence of random numbers is used each time the dataset is chosen for the classifier. The default value is "12345". If left blank, the random number generator is initialized from the system clock, so the sequence of random numbers will be different in each calculation. If you need the results from successive runs of the algorithm to another to be strictly comparable, you should set the seed. To do this, type the desired number you want into the box. This option accepts both positive and negative integers with up to 9 digits.

Random Trees Parameters Dialog

Please see below for options that are unique to the Random Trees Parameters dialog. For remaining option explanations, please see above.

Number of Randomly Selected Features

The Random Trees ensemble method works by training multiple "weak" classification trees using a fixed number of randomly selected features then taking the mode of each class to create a "strong" classifier. The option Number of randomly selected features controls the fixed number of randomly selected features in the algorithm. The default setting is 3.

Random Seed for Featured Selection

If an integer value appears for Feature Selection Random seed, Analytic Solver Data Mining will use this value to set the feature selection random number seed. Setting the random number seed to a nonzero value (any number of your choice is OK) ensures that the same sequence of random numbers is used each time the dataset is chosen for the classifier. The default value is "12345". If left blank, the random number generator is initialized from the system clock, so the sequence of random numbers will be different in each calculation. If you need the results from successive runs of the algorithm to another to be strictly comparable, you should set the seed. To do this, type the desired number you want into the box. This option accepts both positive and negative integers with up to 9 digits.

Ensemble Methods Scoring Dialog

Please see below for options that are unique to the Ensemble Methods Scoring dialog.

Score Training Data

Select these options to show an assessment of the performance of the algorithm in classifying the training data. The report is displayed according to your specifications - Detailed, Summary and Lift Charts. Lift Charts are disabled when the number of classes is greater than 2.

Score Validation Data

These options are enabled when a validation set is present. Select to show an assessment of the performance of the algorithm in classifying the validation data. The report is displayed according to your specifications - Detailed, Summary and Lift Charts. Lift Charts are disabled when the number of classes is greater than 2.

Score Test Data

These options are enabled when a test set is present. Select these options to show an assessment of the performance of the algorithm in classifying the test data. The report is displayed according to your specifications - Detailed, Summary and Lift Charts. Lift Charts are disabled when the number of classes is greater than 2.

Score New Data

See the "Scoring New Data" chapter within the Analytic Solver Data Mining User Guide for more details on the Score New Data options on the Scoring dialog.