If there is a difference in type between a model attribute and an incoming field, then this will be indicated by the label "type-mismatch". Any attributes that don't have a counterpart in the incoming data are indicated by an entry labeled "missing". The fields mapping tab of the scoring dialog shows how the fields in the incoming data from the previous step in the transform - the CSV input step - have been matched with the attributes in the data that the model was trained from. Make sure that the Delimeter text box contains a "," and then click on "Get Fields" to make the CSV input step analyze a few lines of the file and determine the types of the fields. Next, configure the CSV input step to load the "pendigits.csv" file (this is the same data as pendigits.arff, but in csv format). First start Spoon, and then construct a simple transform that links a CSV input step to the Weka scoring step.
#How to give weka jar correct path how to#
A Simple ExampleĪs a simple demonstration of how to use the scoring plugin, you will use the model you created in Weka to score the same data that it was trained on. Using the trained model in Kettle to score new data is simply a matter of configuring the Weka scoring plugin to load and apply the model file you created in the previous section. Save this model to a file called "J48" (a ".model" extension will be added for you).
![how to give weka jar correct path how to give weka jar correct path](https://images.slideplayer.com/16/5192876/slides/slide_10.jpg)
Trained models are stored on disk a serialized Java objects. You can save export any classifier that you have trained in the Classifier panel by right clicking on its entry in the Results History. The default is to perform a 10-fold cross-validation of the learning scheme on the training data, so the statistics on performance that are reported are fairly reliable estimates of what can be expected on future data. The rest of the default settings for evaluation are good for most situations, so you can simply press the "Start" button to launch the training and evaluation of the learning scheme. In this example you will use a decision tree learner (J48).Ĭlicking on the scheme summary will bring up a dialog window that allows you to configure the parameters of J48. In the Classifier panel of the Explorer first choose a learning scheme to apply to the training data. The file will be loaded and summary statistics for the attributes shown in the Preprocess panel. Click on "Open File" and select the "pendigits.arff" file (this file is located in the docs/data directory in the Weka Scoring plugin archive). In this example we will load data from a file in Weka's native arff (Attribute Relation File Format) format.
![how to give weka jar correct path how to give weka jar correct path](https://miro.medium.com/max/1400/1*LR_q2ODYOJD_rDMu9cUh5g.png)
2.2 Loading Data into the Explorerĭata can be imported into the explorer from files (arff, csv or c4.5 format) or from databases.
#How to give weka jar correct path install#
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.
![how to give weka jar correct path how to give weka jar correct path](https://www.oreilly.com/library/view/hands-on-artificial-intelligence/9781789537550/assets/3350a930-f246-43cb-a6fa-e485ba4073f9.png)
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.
![how to give weka jar correct path how to give weka jar correct path](https://i.stack.imgur.com/HsEVU.png)
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.