Following are descriptions of each option available on the Discriminant Analysis dialogs.

  Variables in Input Data

   The variables present in the data set are listed here.

  Selected Variables

   The variables to be included in the Discriminant Analysis algorithm are listed here.

  Output Variable

   The selected output variable is displayed here. (Analytic Solver Pro and XLMiner Pro support a maximum of 30 classes in the output variable. Analytic Solver Platform and XLMiner     Platform allow an unlimited amount of classes in the output variable.)

  #Classes

   This value is the number of classes in the output variable. (Analytic Solver Pro and XLMiner Pro support a maximum of 30 classes in the output variable. Analytic Solver Platform       and XLMiner Platform allow an unlimited amount of classes in the output variable.)

  Specify "Success" class (for Lift Chart)

This option is selected by default. Click the drop-down arrow to select the value to specify a success. This option is enabled when the number of classes for the output variable is equal to 2.

Specify initial cutoff probability for success

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

Canonical Variate

When this option is selected, XLMiner produces the canonical variates for the data based on an orthogonal representation of the original variates. This has the effect of choosing a representation that maximizes the distance between the different groups. For a k class problem, there are k-1 canonical variates. Typically, only a subset of the canonical variates is sufficient to discriminate between the classes. For this example, we have two canonical variates, which means that if we replace the four original predictors by just two predictors, X1 and X2 (which are linear combinations of the four original predictors), the discrimination based on these two predictors will perform similar to the discrimination based on the original predictors.

According to relative occurrences in training data

If this option is selected, XLMiner calculates according to the relative occurrences,and the discriminant analysis procedure incorporates prior assumptions about how frequently the different classes occur. XLMiner assumes that the probability of encountering a particular class in the large data set is the same as the frequency with which it occurs in the Training Data.

Use equal prior probabilities

If this option is selected, XLMiner assumes that all classes occur with equal probability.

User specified prior probabilities

This option is only available when the output variable handles two classes. Select this option to manually enter the desired class and probability value.

Misclassification Costs Of

XLMiner allows the option of specifying the cost of misclassification when there are two classes; where the success class is judged as a failure and the non-success as a success. XLMiner takes into consideration the relative costs of misclassification, and attempts to fit a model that minimizes the total cost.

Partitioning Options

XLMiner V2015 provides the ability to partition a data set from within a classification or prediction method by selecting Partitioning Options on the Step 2 of 3 dialog. If this option is selected, XLMiner partitions the data set (according to the partition options you set) immediately before running the prediction method. If partitioning has already occurred on the data set, this option will be disabled. For more information on partitioning, see the Data Mining Partitioning section.

Linear Discriminant Functions

Select this option to display the functions that define each class in the output.

Canonical Variate Loadings

This option is enabled when Canonical Variate is selected in the Step 2 of 3 dialog. Select this option to view the canonical variates in the output. See Canonical Variate above for more information.

Score Training Data

Select these options to show an assessment of the performance of the Discriminant Analysis algorithm in classifying the training Data. The report is displayed according to the specifications - Detailed, Summary, and Lift Charts. Canonical Scores is available only if Canonical Variate is selected in the Step 2 of 3 dialog. Lift Charts are only available when the Output Variable contains two categories.

Score Validation Data

These options are enabled when a Validation Set is present. Select these options to show an assessment of the performance of the Discriminant Analysis algorithm in classifying the Validation Set. The report is displayed according to your specifications - Detailed, Summary, and Lift Charts. Canonical Scores is available only if Canonical Variate is selected in the Step 2 of 3 dialog. Lift Charts are only available when the Output Variable contains two categories.

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 Discriminant Analysis algorithm in classifying the Test Data. The report is displayed according to your specifications - Detailed, Summary, and Lift Charts. Canonical Scores is available only if Canonical Variate is selected in the Step 2 of 3 dialog. Lift Charts are only available when the Output Variable contains two categories.

Canonical Scores

When this option is selected for either the Training, Validation, or Test Sets, XLMiner reports the scores of the first few observations.

For more information on the options available on the Score Test Data and Score New Data Group, see the Scoring New Data section.