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GLM supports these settings for classification:
Generate Row Diagnostics is off by default. To generate row diagnostics, you must select this option and also specify a Case ID. You can view Row Diagnostics on the Diagnostics tab when you view the model. To further analyze row diagnostics, use a Model Details node to extract the row diagnostics table.
Confidence Level: A positive number that is less than 1.0. Indicates the degree of certainty that the true probability lies within the confidence bounds computed by the model. The default confidence is 0.95.
Reference Class name: The Reference Target Class is the target value used as a reference in a binary logistic regression model. Probabilities are produced for the other (non-reference) class. By default, the algorithm chooses the value with the highest prevalence (the most cases). If there are ties, the attributes are sorted alpha-numerically in ascending order. The default for Reference Class name is System Determined, that is, the algorithm determines the value. To select a specific value, see Choose Reference Value.
Missing Values Treatment: The default is Mean Mode, that is, use mean for numeric values and mode for categorical values. You can also select Delete Row to delete any row that contains missing values. If you delete rows with missing values, the same missing values treatment (delete rows) must be applied to any data that the model is applied to.
Specify Row Weights Column: The default is to not specify a row weights column. The Row Weights Column is a column in the training data that contains a weighting factor for the rows. Row weights can be used as a compact representation of repeated rows, as in the design of experiments where a specific configuration is repeated several times. Row weights can also be used to emphasize certain rows during model construction. For example, to bias the model towards rows that are more recent and away from potentially obsolete data.
Enable Ridge Regression: The default is System Determined, that is, the system determines whether to enable ridge regression; if the system enables ridge regression, the system will specify a ridge value.
Ridge regression is a technique that compensates for multicollinearity (multivariate regression with correlated predictors). Oracle Data Mining supports ridge regression for both regression and classification mining functions.
If you explicitly enable ridge regression, you can use the system-generated ridge value or you can supply your own. If ridge regression is enabled automatically, the ridge value is also calculated automatically.
When ridge regression is enabled, fewer global details are returned. For example, when ridge regression is enabled, no prediction bounds are produced.