https://github.com/davityak03/svm-ml-model-using-different-types-of-kernels-
I have implemented support vector machine classifier on the same dataset but using different kernels and have compared their accuracies with each other
https://github.com/davityak03/svm-ml-model-using-different-types-of-kernels-
linear-kernel poly-kernel rbf-kernel sigmoid-kernel svm
Last synced: about 1 month ago
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I have implemented support vector machine classifier on the same dataset but using different kernels and have compared their accuracies with each other
- Host: GitHub
- URL: https://github.com/davityak03/svm-ml-model-using-different-types-of-kernels-
- Owner: Davityak03
- Created: 2024-05-29T08:31:00.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-29T08:38:38.000Z (over 1 year ago)
- Last Synced: 2025-06-03T01:46:24.014Z (4 months ago)
- Topics: linear-kernel, poly-kernel, rbf-kernel, sigmoid-kernel, svm
- Language: Jupyter Notebook
- Homepage:
- Size: 91.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# SVM-ML-model-using-different-types-of-kernels-
# Support Vector Machine (SVM) with Various Kernels on the Banana Dataset
This repository contains a project that demonstrates the use of Support Vector Machine (SVM) with different kernel functions on the Banana dataset. The kernels used are:
- Radial Basis Function (RBF)
- Linear
- Polynomial (Poly)
- Sigmoid
## Introduction
Support Vector Machines (SVM) are powerful supervised learning models used for classification and regression tasks. This project illustrates how SVM performs with different kernel functions on the Banana dataset, a synthetic dataset commonly used for benchmarking classification algorithms.
## Theory
### Support Vector Machine (SVM)
SVM is a supervised machine learning algorithm used for both classification and regression tasks. However, it is mostly used for classification problems. The goal of the SVM algorithm is to find a hyperplane in an N-dimensional space (N - the number of features) that distinctly classifies the data points.
### Kernels
A kernel is a function that takes low-dimensional input space and transforms it into a higher-dimensional space. In other words, it converts non-separable problems into separable problems by adding more dimensions to it. This makes SVM powerful, as it can handle classification in a high-dimensional space.
The most commonly used kernels are:
- **Linear Kernel**: The simplest kernel function. It is often used when the data is linearly separable.
- **Polynomial Kernel**: Represents the similarity of vectors in a feature space over polynomials of the original variables, allowing the learning of non-linear models.
- **Radial Basis Function (RBF) Kernel**: Also known as the Gaussian kernel. It is a general-purpose kernel; used when there is no prior knowledge about the data.
- **Sigmoid Kernel**: Used as a proxy for neural networks.
## Dataset
The Banana dataset is a synthetic dataset that is commonly used to benchmark machine learning algorithms. It consists of two features and a target label indicating the class. The features are often non-linearly separable, making it an excellent choice for demonstrating the power of different kernel functions in SVM. The datset has been taken from kaggle