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Predictive and Specialized Modeling > Model Comparison > The Model Comparison Report
Publication date: 11/10/2021

Image shown hereThe Model Comparison Report

Figure 11.7 shows an example of the initial Model Comparison report for a continuous response.

Figure 11.7 Initial Model Comparison Report 

Initial Model Comparison Report

The Predictors report shows all responses and all models being compared for each response. The fitting platform that created the predictor column is also listed.

The Measures of Fit report shows measures of fit for each model. The columns are different for continuous and categorical responses.

Measures of Fit for Continuous Responses


The r-squared statistic. In data tables that contain no missing values, the r-squared statistics in the Model Comparison report and original models match. However, if there are any missing values, the r-squared statistics differ.


The square root of the mean squared prediction error. This is computed as follows:

Square and sum the prediction errors (differences between the actual responses and the predicted responses) to obtain the SSE.

Denote the number of observations by n.

RASE is:

RASE = Equation shown here


The average absolute error.


The column that contains frequency counts for each row.

Measures of Fit for Categorical Responses

Entropy RSquare

One minus the ratio of the negative log-likelihoods from the fitted model and the constant probability model. It ranges from 0 to 1.

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 value is 1 for a perfect model, and 0 for a model no better than a constant model. 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).

Mean -Log p

The average of -log(p), where p is the fitted probability associated with the event that occurred.


The root average squared prediction error. For categorical responses, the differences are between 1 and p, the fitted probability for the response level that actually occurred.

Mean Abs Dev

The average of the absolute values of the differences between the response and the predicted response. For categorical responses, the differences are between 1 and p (the fitted probability for the response level that actually occurred).

Misclassification Rate

The rate for which the response category with the highest fitted probability is not the observed category.


The number of observations.

Related Information

Training and Validation Measures of Fit provides more information about measures of fit for categorical responses.

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