(Note, you are not to use modules which provide these functions - that would be too easy (no sklearn.svm, for example) but rather create them yourselves. Due to time constraints, we are concerned more with functionality rather than efficiency. Dataset should be generated via ‘make_blobs’ in sklearn and the number of samples is 100. Please note the generated dataset should be separable. Please refer to (sklearn.datasets.make_blobs — scikit-learn 1.4.1 documentationLinks to an external site.) for more details. here is the link: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs You are expected to use Jupyter notebooks/Colab and Python on this assignment. 1)Please plot the maximum margin separating hyperplane within the dataset using SVM with linear kernel. 2) Remove the closest support vector, redo the task and plot the new maximum margin separating hyperplane. 3) What’s the takeaway by comparing the plots?
(Note, you are not to use modules which provide these functions - that would be too easy (no sklearn.svm, for example) but rather create them yourselves. Due to time constraints, we are concerned more with functionality rather than efficiency.
Dataset should be generated via ‘make_blobs’ in sklearn and the number of samples is 100. Please note the generated dataset should be separable. Please refer to (sklearn.datasets.make_blobs — scikit-learn 1.4.1 documentationLinks to an external site.) for more details.
here is the link: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs
You are expected to use Jupyter notebooks/Colab and Python on this assignment.
1)Please plot the maximum margin separating hyperplane within the dataset using SVM with linear kernel.
2) Remove the closest support
3) What’s the takeaway by comparing the plots?
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