Oracle GLM Weights Options

In a classification model, using weights enables you to specify the relative importance of the various possible target values. Doing so might be useful, for example, if the data points in your training data are not realistically distributed among the categories. Weights enable you to bias the model so that you can compensate for those categories that are less well represented in the data. Increasing the weight for a target value should increase the percentage of correct predictions for that category.

There are three methods of setting weights:

• Based on training data. This is the default. Weights are based on the relative frequencies of the categories in the training data.
• Equal for all classes. Weights for all categories are defined as 1/k, where k is the number of target categories.
• Custom. You can specify your own weights. Starting values for weights are set as equal for all classes. You can adjust the weights for individual categories to user-defined values. To adjust a specific category's weight, select the Weight cell in the table corresponding to the desired category, delete the contents of the cell, and enter the desired value.

The weights for all categories should sum to 1.0. If they do not sum to 1.0, a warning is displayed, with an option to automatically normalize the values. This automatic adjustment preserves the proportions across categories while enforcing the weight constraint. You can perform this adjustment at any time by clicking the Normalize button. To reset the table to equal values for all categories, click the Equalize button.