Category: Data Science

John Mount is not a fan of hyperparamters:

In my opinion one can see this scam of hiding some debt in with an asset spreading.

Earliest modeling systems, such as linear regression, had no hyper-parameters. An under specified algorithm was not considered a fully specified method.

Click through for John’s thoughts on the matter. I’m sympathetic to this argument and want to bring in an extra point John didn’t make. With hyperparameter tuning, you also introduce the risk of spurious correlation between the label and input features. This is particularly relevant if changing the seed or making hyperparameter tweaks results in a major change in model effectiveness.

John Mount answers a common question:

We will phrase the problem in terms of a measurement of error. For example, let’s try to achieve a given square error in a regression problem. That is, how hard it it to estimate a number given a sample from a larger population?

From there, John covers several scenarios and provides us guidance. The answer isn’t “30 observations.” Usually.

Devin Partida bridges the gap:

The world runs on data. Data scientists organize and make sense of a barrage of information, synthesizing and translating it so people can understand it. They drive the innovation and decision-making process for many organizations. But the quality of the data they use can greatly influence the accuracy of their findings, which directly impacts business outcomes and operations. That’s why data scientists must follow strong quality assurance practices.

Read on for seven practices which can help data scientists achieve better outcomes.

John Mount shares some thoughts:

I want to spend some time thinking out loud about linear regression.

As a data science consultant and teacher I spend a lot of time using linear regression and teaching linear regression. I have found each of these pursuits can degenerate into mere doctrine or instructions. “do this,” “expect this,” “don’t do that,” “you should know,” and so on. What I want to do here is take a step back and think out loud about linear regression from first principles. To do attempt this I am going to start with the problem linear regression solves, and try to delay getting to the things so important that “everybody should known them without question.” So let’s think about a few things in a particular order.

For thinking out loud, this is laid out rather well, so give it a read.

John Cook gives us a dose of history and math:

In 1881, astronomer Simon Newcomb noticed something curious. The first pages in books of logarithms were dirty on the edge, while the pages became progressively cleaner in later pages. He inferred from this that people more often looked up the logarithms of numbers with small leading digits than with large leading digits.

Why might this be? One might reasonably expect the numbers that came up in work to be uniformly distributed. But as often the case, it helps to ask “Uniform on what scale?”

Read on for a bit more of the story behind Newcomb’s Benford’s law and a just-so story about differing bases.