Input and Output Data Sets

Since Enterprise Miner is intended especially for the analysis of large data sets, all of the predictive modeling nodes are designed to work with separate training, validation, and test sets. The Data Partition node provides a convenient way to split a single data set into the three subsets, using simple random sampling, stratified random sampling, or user-defined sampling. Each predictive modeling node also enables you to specify a fourth scoring data set that is not required to contain the target variable. These four different uses for data sets are called the roles of the data sets. For the training, validation and test sets, the predictive modeling nodes can produce two output data sets: one containing the original data plus scores (predicted values, residuals, classification results, and so on), the other containing various statistics pertaining to the fit of the model (the error function, misclassification rate, and so on). For scoring sets, only the output data set containing scores can be produced.

Output data sets containing scores have new variables with names usually formed by adding prefixes to the name of the target variable or variables and, in some situations, the input variables or the decision data set.

Usually, for categorical targets, the actual target values are dummy 0/1 variables. Hence, the outputs (P_) are estimates of posterior probabilities. Some modeling nodes might allow other ways of fitting categorical targets. For example, when the Regression node fits an ordinal target by linear least squares, it uses the index of the category as the actual target value, and hence does not produce posterior probabilities.

Outputs (P_) are always predictions of the actual target variable, even if the target variable is standardized or otherwise rescaled during modeling computations. Similarly, plain residuals (R_) are always the actual target value minus the output. Plain residuals are not multiplied by error weights or by frequencies.

For least squares estimation, the error function variable (E_) contains the squared error for each case. For generalized linear models or other methods based on minimizing deviance, the E_ variable is the deviance. For other types of maximum likelihood estimation, the E_ variable is the negative log likelihood. In other words, the E_ variable is whatever the training method is trying to minimize the sum of.

The deviance residual is the signed square root of the value of the error function for a given case. In other words, if you square the deviance residuals, multiply them by the frequency values, and add them up, you will get the value of the error function for the entire data set. Hence, if the target variable is rescaled, the deviance residuals are based on the rescaled target values, not on the actual target values. However, deviance residuals cannot be computed for categorical target variables.

For categorical target variables, names for dummy target variables are created by concatenating the target name with the formatted target values, with invalid characters replaced by underscores. Output and residual names are created by adding the appropriate prefix (P_, R_, and so on) to the dummy target variable names. The F_ variable is the formatted value of the target variable. The I_ variable is the category that the case is classified into--also a formatted value. The I_ value is the category with the highest posterior probability. If a decision matrix is used, the D_ value is the decision with the largest estimated profit or smallest estimated loss. The D_ value might differ from the I_ value for two reasons:

However, the I_ value can depend indirectly on the decision matrix when the decision matrix is used in model estimation or selection.

Predicted values are computed for all cases. The model is used to compute predicted values whenever possible, regardless of whether the target variable is missing, inputs excluded from the model (for example, by stepwise selection) are missing, the frequency variable is missing, and so on. When predicted values cannot be computed using the model — for example, when required inputs are missing — the P_ variables are set according to an intercept-only model:

Scored output data sets also contain a variable named _WARN_ that indicates problems computing predicted values or making decisions. _WARN_ is a character variable that either is blank, indicating there were no problems, or that contains one or more of the following character codes:

Regardless of how the P_ variables are computed, the I_ variables as well as the residuals and errors are computed exactly the same way given the values of the P_ variables. All cases with nonmissing targets and positive frequencies contribute to the fit statistics. It is important that all such cases be included in the computation of fit statistics because model comparisons must be based on exactly the same sets of cases for every model under consideration, regardless of which modeling nodes are used.

The output data sets containing fit statistics produced by the Regression node and the Decision Tree node have only one record. Since the Neural Network node can analyze multiple target variables, it produces one record for each target variable and one record for the overall fit; the variable called _NAME_ indicates which target variable the statistics are for.

The fit statistics for training data generally include the following variables, computed from the sum of frequencies and ordinary residuals:

Note that _DFT_, _DIV_, and _NOBS_ can all be different when the target variable is categorical.

The following fit statistics are computed according to the error function (such as squared error, deviance, or negative log likelihood) that was minimized:

For a categorical target variable, the following statistics are also computed:

When decision processing is done, the statistics in the following table are also computed for the training set. The profit variables are computed for a profit or revenue matrix, and the loss variables are computed for a loss matrix:

For a validation data set, the variable names contain a V following the first underscore. For a test data set, the variable names contain a T following the first underscore. Not all the fit statistics are appropriate for validation and test sets, and adjustments for model degrees of freedom are not applicable. Hence, ASE and MSE become the same. For a validation set, the following fit statistics are computed:

For a validation set and a categorical target variable, the following fit statistics are computed:

When decision processing is done, the following statistics are also computed for the validation set:

Cross validation statistics are similar to the above except that the letter X appears instead of V. These statistics appear in the same data set or data sets as fit statistics for the training data. For a test set, the following fit statistics are computed:

For a test data set and a categorical target variable, the following fit statistics are computed:

When decision processing is done, the following statistics are also computed for the test set: