Machine Learning on a Cancer Dataset - Part 31

This is the final tutorial on support vector machines with scikit-learn on the cancer dataset.

We're doing a recap on the pluses and minuses of support vector machines as machine learning algorithms. Some the pluses include:

• versatility
• work well on low-dimensional data
• work relatively good on high-dimensional data of small size

Some of their minuses include:

• don't work so good on large scale high-dimensional data
• may need pre-processing
• can be hard to inspect
• not so easy to understand by non-experts

In the video I also discuss about some of the parameters that can be adjusted to tune these models, as well as alternative models to SVMs. So, please see the full walk-through below.

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Previous videos in this series:

1. Machine Learning on a Cancer Dataset - Part 30

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Cristi Vlad, Self-Experimenter and Author