Which SVM classifier is better: a made from scratch one of sklearn’s off the shelf version?

Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outlier detection. SVMs are one of the more robust prediction methods, being based on statistical learning frameworks.

Given a set of training examples, with each belonging to one of two categories, a SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary classifier. SVM maps training examples to points in space in order to maximise the width of the gap between the two categories. New examples are mapped into that same space and predicted to belong to a category based on which side of the gap they fall.

The advantages of SVMs are:-

1. They are effective in high dimensional spaces.
2. They are effective in cases where the number of dimensions is greater than the number of samples.
3. They use a subset of training points in the decision function, called vectors, making them memory efficient.
4. They are versatile because…