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Practical:-5
AIM:- Visual Programming with Orange Tool
This blog is all about how to split data into training and testing using the Orange tool. We will also learn more about Test & Score Widget. We will also explore the cross-validation method using the Orange tool.
What is Train Test Split?
The train-test split is a technique for evaluating the performance of a machine learning algorithm. It can be used for classification or regression problems and can be used for any supervised learning algorithm. The procedure involves taking a dataset and dividing it into two subsets.
For the Train Test Split, I used the below workflow.
Here as usual I load heart_disease.tab dataset in the File widget which comes with the orange tool.
Here you can see the image of data sampler for heart_disease data set.As you can see I split the data into 70:30 ratio i.e 70% Train Data and 30% Test Data. On the bottom, you can see 213 data points use for Training and 90 data points used for testing from a Total of 303 data points.
Now for model creation, I used KNN and Logistic Regression Widgets. These all the widgets are machine learning algorithms. Connect all the widgets with Test & Score Widget.
Above image, you can see test and train score for the both of ML model.
Above image, you can see cross validation for both of model.
After that Test & Score widget connects with the Confusion Matrix in which we can see the result and after that, from the confusion matrix.
Confusion Matrix for KNN:-
Confusion Matrix for Logistic Regresssion:-
Conclusion:
I hope now you can work by yourself in the orange tool. I tried to cover as many things as I can.
Check out more features of the Orange tool here.
GitHub Link:-
https://github.com/YagnikBavishi/DataScience/tree/main/Visual_Programming
