Introduction I would like to talk about the nature of supervised machine learning and overfitting. One of the cornerstones of our data science intensives is giving the participants the experiences of a data scientist in a safe controlled environment. We hope by working examples they can quickly get to the […]

Estimated reading time: 33 minutes

Why a mere accurate classification rule may not meet your business needs. And why you should insist on a model that returns numeric scores for classification problems. (link)

Estimated reading time: 20 seconds

Let’s please stop saying somebody isn’t a data scientist if they haven’t memorized the innards of one obscure machine learning algorithm, or blow the right smoke during an interoo (“Kangaroo interview”, thanks Jim Ruppert for this term!). Let us, instead, think of the data scientist as the bus driver. It […]

Estimated reading time: 1 minute

I am sharing some rough notes (in R and Python) here on how while dot(a, b) fulfills “Mercer’s condition” (by definition!, and I’ll just informally call these beasts a “Mercer Kernel”), the seemingly harmless variations abs(dot(a, b)) relu(dot(a, b)) are not Mercer Kernels (relu(x) = max(0, x) = (abs(x) + […]

Estimated reading time: 2 minutes

I am sharing a new free video where I work through a great common argument that bounds expected excess generalization error as a ratio of model complexity (in rows) over training set size (again in rows), independent of problem dimension. (link) For more of my notes on support vector machines […]

Estimated reading time: 34 seconds

Our book, Practical Data Science with R, just had its first year anniversary! The book is doing great, if you are working with R and data I recommend you check it out. (link)

Estimated reading time: 22 seconds