1. Documentation
2. 10.1 (Latest)10.09.109.99.89.79.69.59.49.39.29.19.08.28.18.07.6
3. Studio
4. Getting started
5. Samples repository

Using the RapidMiner Studio Built-in Samples

When you have completed the tutorials, you can use RapidMiner Studio's built-in samples repository, with explanatory help text, for more practice exercises. The sample data and processes are located in theRepositorypanel:

• Thedatafolder contains a dozen different data sets, which are used by the sample exercises. They contain a variety of different data types.
• Theprocessesfolder contains over 130 sample processes, organized by function, that demonstrate preprocessing, visualization, clustering, and many other topics.

To use the samples, expand theprocessesfolder.

There are two mechanisms for using these processes:

• double-clickto display the individual operators with help text. This method is best for learning.
• drag-and-dropto have the process immediately available for running.

Double-click for additional detail

You can learn a lot by double-clicking a sample process.

1. Select a process. This example uses01_DecisionTree.

2. Double-click on the process name. RapidMiner opens the process and displays it on the canvas:

   

3. 参数设置一个操作符,单击on it. For example, if you click on theRetrieveoperator, theParameterspanel reports the data set in use ("Golf"):

   

4. Clickto run the process.

Drag-and-drop for efficiency

If you drag a process from the repository onto the canvas, things look different than they did above. RapidMiner automatically creates anExecuteoperator, which, when run, executes the process you dragged in.

Again, this example uses01_DecisionTree.

1. Drag01_DecisionTreeonto the canvas.

   

2. Notice that what you see is theExecuteoperator. You cannot see the operators that make up the process. For that, you would need to double-click the process in theRepositorypanel.

3. Connect the results (res) port of theExecuteoperator to the results port of the process and click runto run the process.

As you gain experience and begin to design complicated processes with multitudes of operators, you will want to build in some structure. By saving multi-operator processes, you can reuse them as needed. For example, if you do a lot of analysis, you can make one process for updating data, one for data preprocessing, one for model creation, one for model performance checks, etc. Save each process to the repository and drag them to the canvas as needed. Then, your main process will contain just interconnectedExecuteoperators. Neat and tidy.