Bootstrap aggregating (bagging) is a technique to improve predictive performance while also gaining insight into the reliability of predictions. Bagging is especially useful in unstable methods, including neural networks, classification trees, and regression trees.

Bagging creates M training data sets by sampling with replacement from the original data. All training data sets are of the same size as the original. For each training data set, a model is fit using the analysis platform, and predictions are made. Therefore, there are a total of M predictions for each observation in the original data set. The final prediction is the average of the M predictions.

Bagging is available in many analysis platforms. To use bagging, select Save Bagged Predictions from the Prediction Profiler red triangle menu. A window appears with the following options for Bagging:

Suppose that Fractional Weights is selected. For each bootstrap iteration, each observation that is used in the report is assigned a nonzero weight. These weights sum to n, the number of observations used in the model. For more information about how the weights are calculated and used, see Calculation of Fractional Weights in Bootstrapping.

Bagging automatically creates new columns in the original data table. All M sets of bagged predictions are saved as hidden columns. The final prediction is saved in a column named “Pred Formula <colname> Bagged Mean”. The standard deviation of the final prediction is saved in a column named “<colname> Bagged Std Dev”. The standard error of the bagged mean is saved in a column named “StdError <colname> Bagged Mean.” The standard error is the standard deviation divided by . Here, <colname> identifies the column in the report that was bagged.