Category: Data Science

Steven Sanderson unleashes the power of the triangle:

The Triangular Distribution is a continuous probability distribution with a triangular shape, hence the name. It is defined by three parameters: min, max, and mode. These parameters determine the range of values the distribution can take and the most likely value within that range. In mathematical terms, the probability density function (PDF) of the Triangular Distribution is given by:

Read on to see the definition, as well as how you can use the four functions around the Triangular Distribution.

By the way, the best-known case of the Triangular Distribution is combining the results of two fair dice, which gives us a peak at the number 7 (1/6 of the time) for a pair of fair, six-sided dice and moving symmetrically down from there, so p(6) = p(8), p(5) = p(9), and so on.

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Steven Sanderson isn’t satisfied with the binomial:

The multinomial distribution is a probability distribution that describes the probability of obtaining a specific number of counts for k different outcomes, when each outcome has a fixed probability of occurring.

In R, we can use the rmultinom() function to simulate random samples from a multinomial distribution, and the dmultinom() function to calculate the probability of a specific outcome.

Click through to see how you can build a multinomial distribution and what the difference is between rmultinom() and dmultinom().

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Steven Sanderson digs into the uniform distribution:

Randomness is an essential part of many statistical and machine learning tasks. In R, there are a number of functions that can be used to generate random numbers, but the runif() function is the most commonly used.

Something mildly embarrassing for me is that it took me a while to figure out why they call the command runif(). That’s because, at first, I didn’t pronounce it r unif but rather run if.

In reality, *unif() means “uniform distribution” and r stands for “random number.” There are several other functions based on the uniform distribution and Steven looks at those as well in this post.

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I decided to test a common narrative:

A common theme among Buffalo Bills fans is the idea that the Bills run too many plays out of shotgun near the opposing team’s goal line, and this is hampering their ability to score points. Instead, these fans argue, they should run from under center, either a direct handoff or a quarterback sneak. If you were to press fans on this, I believe you’d also hear that the Bills are unique, or at least uniquely bad, at running such plays.

I’m going to use the nflfastR package to analyze play-by-play data and see just how well this bit of fan wisdom holds up.

Spoiler alert: it doesn’t.

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Steven Sanderson performs a logistic regression:

Logistic regression is a statistical method used for predicting the probability of a binary outcome. It’s a fundamental tool in machine learning and statistics, often employed in various fields such as healthcare, finance, and marketing. We use logistic regression when we want to understand the relationship between one or more independent variables and a binary outcome, which can be “yes/no,” “1/0,” or any two-class distinction.

Click through to learn how to do this.

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Steven Sanderson puts a halt to things:

When working with time series data, one common challenge is dealing with non-stationary data. Non-stationary time series can be a headache for analysts, but fear not, because we have a handy tool to make your life easier. Say hello to the auto_stationarize() function from the {healthyR.ts} package.

Read on to learn why you want stationary data for time series analysis and how the auto_stationarize() function works.

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Steven Sanderson isn’t just spinning in place:

Before we delve into the ts_adf_test() function, let’s understand the concept behind it. The Augmented Dickey-Fuller (ADF) test is a crucial tool in time series analysis. It’s like the Sherlock Holmes of time series data, helping us detect whether a series is stationary or not. Stationarity is a fundamental assumption in time series modeling because many models work best when applied to stationary data.

So, why “Augmented”? Well, it’s an extension of the original Dickey-Fuller test that accounts for more complex relationships within the time series data.

Click through to see how you can use the ts_adf_test() function to get a better feel for whether a time series is stationary.

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Michael Mayer has a new package:

The current version offers:

• H statistics per feature, feature pair, and feature triple
• multivariate predictions at no additional cost
• a convenient API
• other important tools from explainable ML:
  • performance calculations
  • permutation importance (e.g., to select features for calculating H-statistics)
  • partial dependence plots (including grouping, multivariate, multivariable)
  • individual conditional expectations (ICE)
• Case-weights are available for all methods, which is important, e.g., in insurance applications.

Click through for an example of how it works, followed by some simple benchmarking to give you an idea of how it performs compared to similar tools.

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