Neural Networks Using Rapidminer Tutorial New Version
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Okay friends, in this article I will share educational experiences that might be useful for you.
Artificial Neural Networks (ANN) is a method that was originally inspired by the nervous system of living things. ANN appears as an alternative to conventional approaches which are usually less flexible to changes in the structure of the problem.
How to Apply the ANN Algorithm using the Rapidminer application?
That's about the question that is often asked to me for those of you who are still in college.
Okay, first before you enter the rapidminer process, you must and really must have an existing training data set on the criteria that allow the ANN algorithm to be applied. Usually the data required is numeric data.
After the data is ready, you have to import the data set below into the repository at Rapiminer Studio. If you can't, first learn the import tutorial here ----.
1. After the data has been imported, you drag it to the process page in the middle.
2. Next, on the left sidebar menu in the operator box, you can search and find menus such as Neurat Net, Apply Model, Performance and Split Data.
- Neural Net (ANN algorithm)
- Apply Model (for modeling)
- Performance (measuring performance)
- Split Data (to distribute training data and test data)
Setting Parameters Split Data
- After you have found these models, then do the following steps
- Connect retrieving your data set with the split data operator then set the parameters by clicking 2x the split data operator and immediately focus on the right sidebar
- Set the sampling type as needed
- Click edit enumeration to distribute training data and test data
- The first step, select Add Entry, then enter what percentage of training data will be taken (write in decimal form)
- The second step, select Add Entry, and specify the percentage of data that will be used as the test data set
3. Next, you also set the parameters for the Neural Net operator in order to determine the attributes of the calculation or the formation of a predictive model from the algorithm.
4. The next step, connect the operators like this.
5. The final step, you run it by pressing the blue start button at the top of the process page.
The results that will be displayed on the results page are the results of the confusion matrix calculation which consists of the values for accuracy, precision, recall and Area Under Curve (AUC).
The value will appear in the Performance Vector tab, and for models that have been formed by the ANN algorithm, it will appear on the Improved NeuralNet (Neural Net) tab.
Well, it's finished and it's finished friends, how complicated or simple. Yes, it all depends on your own point of view. To what extent do you want to learn and continue to move forward
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So, thank you for stopping by and believe you have learned here, continue to monitor the Educational Experience because My System is Your Information
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