Visual Programming using Orange Tool
This blog is for educational purposes only to learn about visual programming using Orange Tool.
What is Visual Programming?
It is a type of programming language which anyone can understand. Anyone can describe the process using illustrations. In text-programming language, a programmer things like a computer whereas in visual programming, a programmer describes a process that makes sense to a human being.
Orange is a great data mining tool for beginners as well as for expert data scientists. Thanks to its user interface users can focus on data analysis instead of on laborious coding, making the construction of complex data analytics pipelines simple.
Implementation
Step 1: We will use the File widget in the canvas and load the inbuilt zoo dataset in the workflow. You can also import your own dataset using browsing or using URL. This dataset is of type classification and has 16 attributes & 101 data instances.
Step 2: Now we have to end this data as an input to the Data Sampler.
Data Sampler selects a subset of data instances from an input data set. It outputs a sampled and a complementary data set. The output is processed after the input data set is provided and Sample Data is pressed.
Here, I have kept 75% of the data for training & the rest for testing. It is suggested to keep the training dataset between 70–90 percent.
Step 4: We need to send this sample data from Data Sampler to Test and Score. The widget tests learning algorithms. Different sampling schemes are available, including using separate test data.
Also, connect machine learning models to the Test and Score widget. I have used 3 different algorithms like Neural Network, Logistic Regression, SVM for demonstration. These 3 algorithms are the most suitable for classification.
Here, the Data Sampler widget passes 75% of data for training and the rest 30% for testing to the Test and Score widget.
Step 5: Now get the comparison scores of the three different algorithms by testing on the train data. To do so double click on the Test and Score widget and choose the option of Test on train data there and get the scores for all three algorithms.
So till now, we have compared 3 different machine learning algorithms for the classification datasets. Using the orange tool, analysis becomes a lot simpler and easy to carry out.
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