Sequence Node Model Options

Model name. You can generate the model name automatically based on the target or ID field (or model type in cases where no such field is specified) or specify a custom name.

Use partitioned data. If a partition field is defined, this option ensures that data from only the training partition is used to build the model.

Minimum rule support (%) You can specify a support criterion. Rule support refers to the proportion of IDs in the training data that contain the entire sequence. If you want to focus on more common sequences, increase this setting.

Minimum rule confidence (%) You can specify a confidence criterion for keeping sequences in the sequence set. Confidence refers to the percentage of the IDs where a correct prediction is made, out of all the IDs for which the rule makes a prediction. It is calculated as the number of IDs for which the entire sequence is found divided by the number of IDs for which the antecedents are found, based on the training data. Sequences with lower confidence than the specified criterion are discarded. If you are getting too many sequences or uninteresting sequences, try increasing this setting. If you are getting too few sequences, try decreasing this setting.

Note: If necessary, you can highlight the value and type in your own value. Be aware that if you reduce the confidence value below 1.0, in addition to the process requiring a lot of free memory, you might find that the rules take an extremely long time to build.

Maximum sequence size You can set the maximum number of distinct items in a sequence. If the sequences of interest are relatively short, you can decrease this setting to speed up building the sequence set.

Predictions to add to stream Specify the number of predictions to be added to the stream by the resulting generated Model node. For more information, see Sequence Model Nuggets.