Table of Contents
At a glance
Description
The bartScore action scores a data table using a previously fitted Bayesian additive regression trees (BART) model. It generates predicted values, residuals, and confidence limits for each observation in the input data.
| Parameter | Description |
|---|---|
| alpha | Specifies the significance level for constructing equal-tail credible limits. Default is 0.05. |
| avgOnly | When set to FALSE, includes predictions from each MCMC sample in the output, not just the average. Default is TRUE. |
| casOut | Specifies the output table to store observation-wise statistics. |
| copyVars | A list of variables to copy from the input data table to the output table. |
| into | Specifies the name of the variable for predicted class labels in classification models. |
| intoCutpt | Specifies the cutoff probability for classifying observations. Default is 0.5. |
| lcl | Specifies the name for the lower credible limit variable. |
| pred | Specifies the name for the predicted value variable. Default is 'Pred'. |
| resid | Specifies the name for the residual variable. |
| restore | Specifies the input CAS table that contains the fitted model information from a previous training run (e.g., from bartGauss or bartProbit). |
| seed | Specifies the random seed for the pseudorandom number generator. Default is 0 for a random seed. |
| table | Specifies the input data table to be scored. |
| ucl | Specifies the name for the upper credible limit variable. |
Data Creation for Scoring
This example first creates a training dataset 'mycas.train_data' and a scoring dataset 'mycas.score_data'. A BART model is trained using the 'bartGauss' action, and the model is saved to 'mycas.bart_model'. The 'score_data' table is then used for scoring.
| 1 | PROC CAS; |
| 2 | DATA mycas.train_data; |
| 3 | DO i = 1 to 100; |
| 4 | x1 = rand('UNIFORM'); |
| 5 | x2 = rand('UNIFORM'); |
| 6 | x3 = rand('NORMAL'); |
| 7 | y = 10 * sin(3.14 * x1 * x2) + 20 * (x3 - 0.5)**2 + 10 * x1 + 5 * x2 + rand('NORMAL'); |
| 8 | OUTPUT; |
| 9 | END; |
| 10 | RUN; |
| 11 | |
| 12 | DATA mycas.score_data; |
| 13 | DO i = 1 to 50; |
| 14 | x1 = rand('UNIFORM'); |
| 15 | x2 = rand('UNIFORM'); |
| 16 | x3 = rand('NORMAL'); |
| 17 | OUTPUT; |
| 18 | END; |
| 19 | RUN; |
| 20 | |
| 21 | bart.bartGauss / |
| 22 | TABLE='train_data', |
| 23 | inputs={{name='x1'}, {name='x2'}, {name='x3'}}, |
| 24 | target='y', |
| 25 | saveState={name='bart_model', replace=true}; |
| 26 | QUIT; |
Examples
Scores the 'score_data' table using the saved model 'bart_model' and saves the results to 'mycas.bart_scored_simple'.
| 1 | |
| 2 | PROC CAS; |
| 3 | bart.bartScore / TABLE='score_data', restore='bart_model', casOut={name='bart_scored_simple', replace=true}; |
| 4 | |
| 5 | QUIT; |
| 6 |
An output table 'mycas.bart_scored_simple' is created containing the predicted values for each observation in 'mycas.score_data'.
This example scores the 'score_data' table, calculates 90% credible limits (alpha=0.1), and includes the original input variables in the output. Custom names are provided for the predicted value, lower limit, upper limit, and residual variables.
| 1 | |
| 2 | PROC CAS; |
| 3 | bart.bartScore / TABLE='score_data', restore='bart_model', casOut={name='bart_scored_detailed', replace=true}, copyVars={'x1', 'x2', 'x3'}, alpha=0.1, pred='Predicted_Y', lcl='Lower_CI', ucl='Upper_CI', resid='Residuals'; |
| 4 | |
| 5 | QUIT; |
| 6 |
An output table 'mycas.bart_scored_detailed' is created. It includes the original variables (x1, x2, x3), the predicted values ('Predicted_Y'), the 90% lower ('Lower_CI') and upper ('Upper_CI') credible limits, and the residuals ('Residuals').
This example scores the data and includes the predictions from every MCMC sample in the output table, in addition to the average prediction. This is useful for detailed posterior distribution analysis.
| 1 | |
| 2 | PROC CAS; |
| 3 | bart.bartScore / TABLE='score_data', restore='bart_model', casOut={name='bart_scored_allsamples', replace=true}, avgOnly=false; |
| 4 | |
| 5 | QUIT; |
| 6 |
The output table 'mycas.bart_scored_allsamples' will contain the average predicted value ('Pred') and additional columns representing the prediction from each individual MCMC sample (e.g., _S_1, _S_2, etc.).
FAQ
Associated Scenarios
Use Case
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