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Publication date: 05/05/2023

Show Fit Details

The Fit Details report in the Partition platform contains the following measures of fit:

Entropy RSquare

A measure of fit that compares the log-likelihoods from the fitted model and the constant probability model. Entropy RSquare ranges from 0 to 1, where values closer to 1 indicate a better fit. See Entropy RSquare.

Generalized RSquare

A measure that can be applied to general regression models. It is based on the likelihood function L and is scaled to have a maximum value of 1. The Generalized RSquare measure simplifies to the traditional RSquare for continuous normal responses in the standard least squares setting. Generalized RSquare is also known as the Nagelkerke or Craig and Uhler R2, which is a normalized version of Cox and Snell’s pseudo R2. See Nagelkerke (1991). Values closer to 1 indicate a better fit.

Mean -Log p

The average of -log(p), where p is the fitted probability associated with the event that occurred. Smaller values indicate a better fit.

RASE

The root average square error, where the differences are between the response and p (the fitted probability for the event that actually occurred). Smaller values indicate a better fit.

Mean Abs Dev

The average of the absolute values of the differences between the response and p (the fitted probability for the event that actually occurred). Smaller values indicate a better fit.

Misclassification Rate

The rate for which the response category with the highest fitted probability is not the observed category. Smaller values indicate a better fit.

Confusion Matrix Report

The Confusion Matrix report shows matrices for the training set and for the validation and test sets (if defined). The Confusion Matrix Report contains confusion matrices and confusion rates matrices. A confusion matrix is a two-way classification of actual and predicted responses. A confusion rates matrix is equal to the confusion matrix, with the numbers divided by the row totals.

Decision Matrix Report

When a profit matrix is defined, the partition algorithm uses the values in the matrix to calculate the profit for each decision. You can define a profit matrix with a Profit Matrix column property or by specifying costs using the Specify Profit Matrix option. See Specify Profit Matrix.

When you select Show Fit Details, a Decision Matrix report appears. In the Decision Matrix report, the decision counts reflect the most profitable prediction decisions based on the weighting in the profit matrix. The report gives Decision Count and Decision Rate matrices for the training set and for validation and test sets (if defined). For reference, the profit matrix is also shown.

Note: If you change the weights in your Profit Matrix using the Specify Profit Matrix option, the Decision Matrix report automatically updates to reflect your changes.

Decision Count Matrix

Shows a two-way classification with actual responses in rows and classification counts in columns.

Specified Profit Matrix

Gives the weights that define the Profit Matrix.

Decision Rate Matrix

Shows rate values corresponding to the proportion of a given row’s observations that are classified into each category. If all observations are correctly classified, the rates on the diagonal are all equal to one.

Tip: You can obtain a decision rate matrices for a response using the default profit matrix with costs of 1 and -1. Select Specify Profit Matrix from the red triangle menu, make no changes to the default values, and click OK.

The matrices are arranged in two rows:

The Decision Count matrices are in the first row.

The Specified Profit Matrix is to the right in the first row.

The Decision Rate matrices are in the second row.

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