In this project, we aim to explore the use of Gaussian RBF in creating a decision boundary for classification tasks using Python's scikit-learn library within a Jupyter notebook environment.
The repository contains a Jupyter notebook along with all the necessary data and code to replicate our experiments and reproduce our results. We demonstrate how to use the svm module from scikit-learn to train an SVM classifier with the Gaussian RBF kernel and visualize the decision boundary in 2D and 3D.
The libraries we import at the beginning of the script have the following functions:
%matplotlib inline is a Jupyter notebook magic command that allows for inline plotting in the notebook. This enables us to visualize the decision boundary in the notebook itself.
matplotlib.pyplot is a plotting library for creating static, animated, and interactive visualizations in Python. sklearn is the scikit-learn library that provides a wide range of machine learning algorithms, including SVM.
numpy is a Python library used for working with arrays, matrices, and other numerical operations. pandas is a data manipulation library used for data analysis and cleaning.
seaborn is a visualization library built on top of matplotlib that provides an interface for creating informative and attractive statistical graphics.
scipy.io is a module in SciPy library used for loading and saving Matlab files.
We hope that this project will serve as a useful resource for anyone looking to learn about Gaussian RBF and SVMs or implement a decision boundary for their own classification tasks.
Thank you for checking out our project and we welcome any feedback or contributions!