Classification
Access the notebooks with the option to run them online using Binder: 
The examples are organized below:
Data Normalization:
Shows how to normalize data using negative selection classes. In the real-valued version, the data is normalized between 0 and 1. In the binary version, it is normalized into a bit vector.
K-fold Cross Validation with 50 Interactions:
In this example, the data is divided into training and test sets and model performance is evaluated by cross-validation. So with dividing the training data into k parts. In each iteration, 10% of the training data is reserved for testing.
Training:
The trained model is tested in this example with all available training data.
The examples below show various functionality of negative selection classes so that you know how to use them in your project. Feel free to explore these examples and adapt them as needed to meet your specific needs.
Examples:
📄️ Negative Selection Algorithm
On this page, you will find a collection of practical examples that demonstrate how to use the negative selection classes implemented in our package.
📄️ Clonal Selection Algorithm
This page presents a collection of practical examples showcasing how to use the Clonal Selection Algorithm.