### Support Vector Machines – References

Anscombe’s quartet on Wikipedia Support Vector Machines on scikit learn Support Vector Regression (SVR) using linear and non-linear kernels on scikit learn SVM without tears […]

Anscombe’s quartet on Wikipedia Support Vector Machines on scikit learn Support Vector Regression (SVR) using linear and non-linear kernels on scikit learn SVM without tears […]

When dealing with non-separable data, another approach is to introduce a cost associated with making a classification mistake at (see below). The exact cost is […]

The best separating strip may not be linear, especially when the underlying data structure is too complex. The solution is “simple”: we devise a transformation […]

Consider again the mortgage default dataset from the previous post. The decision boundary is not exact: one defaulter falls in the non-default region. A straight […]

In his 2006 offering, Edward Tufte highlights his Fundamental Principles of Analytical Design. In this article, I will showcase these principles and provide an illustrating […]

Support Vector Machines (SVMs) are a family of supervised learning methods which determines the “best” curve/surface separating/classifying the data into disjoint classes.

Texts S.Few [2012], Show Me the Numbers: Designing Tables and Graphs to Enlighten, Amazon.ca S.Few [2009], Now You See It: Simple Visualization Techniques for Quantitative […]

I mentioned in a prior post that the basic principles of Bayesian data analysis are simple, and they are. They are also not controversial: frequentists […]

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