Batch query

Introduction

A Batch query allows multiple cases to be queried.

The resulting queries can be charted, used to generate summary statistics, used to generate a confusion matrix or lift chart for classification analysis, used to evaluate regression models, evaluate anomaly detection trends and more.

Batch query

NOTE

If you are trying to predict a variable, but have the expected predicted value in the data set, do not map it to the variable. Instead include the data by checking it on the Information tab. This will allow you to perform classification analysis if you export the data. An alternative to this approach is to use the Retract Evidence feature, which will assume no evidence is present for the variable being predicted.

Queries

The following list describes each query type.

Retract evidence

Consider the prediction of variables X, Y and Z, where all variables have data mapped to them. If the Retract button is checked, then the prediction of X will ignore any evidence set on X, using only evidence set on Y and Z. The prediction of Y will only use evidence set on X and Z, and the prediction of Z will only use evidence set on X and Y. See Retract evidence for more information.

State Values

When the State values check box is set to true, the output for any discrete variables with a suitable BayesServer.StateValueType are the state values, rather than the state name or index.

NOTE

Note that this option overrides the State Names option.

State Names

When the State Names check box is set to true, the output for any discrete variables is the name of the discrete state. When set to false, the output is the zero based index of the state.

NOTE

Note that this option may be overridden by the State Values option.

Skip if query error

This option affects the outcome if an error occurs during the batch query. When true, processing will continue, but cases with errors are not output. A certain number of errors will be displayed at the end of processing. When false, the output stops at the point of the first error encountered.

Most probable explanation

Most probable explanation (MPE), also known as max propagation, computes the most probable configuration of variables that do not have evidence. See Most probable explanation for more information.

Temporal options

Allow the minimum and maximum times to be set, when running batch time series queries on networks with temporal nodes (i.e. dynamic Bayesian networks).

Algorithm

This option determines the algorithm used, when calculating the batch query.

Export

To perform further analysis or visualize the results, you can export the batch query results.