This project aims at introducing the notion of Kernel method. This notion of kernel method is widely used in Machine Learning; It is used to model datasets that are non linearly separable. It is almost similar to the notion of Neural Networks, It allows to project the dataset in a space where data are linearly Separable. In kernel Method, we use kernel trick procedure.
Kernel | RBF |
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Refer to this link to get the data.
- Understand the Notion of Kernel Method.
- Be able to introduce the kernel trick.
- Implement SVM with the Kernel methods.
- Play around with the different type of kernel in the datasets.
- Compare the results with another type of model, particularly Neural Networks on the same dataset.
$ conda create -n yourenvname
$ conda activate yourenvname
To run this, make sure to install all the requirements by:
$ conda install --file requirements.txt
$ python3 main.py
$ python3 main.py
$ python3 main.py