![]() ![]() ![]() 2.2 Loading Data into the ExplorerÄata can be imported into the explorer from files (arff, csv or c4.5 format) or from databases. You'll need to download and install Weka 3.7.11, recreate the model, and then load it into the Weka scoring step. Models created in Weka 3.6.x are not compatible. ![]() Within PDI 5.3, we ship the Weka 3.7.11 core jar file in the Weka plugins in PDI. The Weka scoring plugin provides the ability to attach a predicted label (classification/clustering), number (regression) or probability distribution (classification/clustering) to a row of data. As of Weka version 3.6.0, it can also handle certain types of models expressed in the Predictive Modeling Markup Language (PMML). The Weka scoring plugin can handle all types of classifiers and clusterers that can be constructed in Weka. "Scoring" simply means attaching a prediction to an incoming row of data. The Weka scoring plugin is a tool that allows classification and clustering models created with Weka to be used to "score" new data as part of a Kettle transform. 4.2 Updating Incremental Models on the Incoming Data Stream.4.1 Storing Models in Kettle XML Configuration File or Repository.
0 Comments
Leave a Reply. |