Press "Enter" to skip to content

Category: Data Science

Stratified Sampling in R

Published 2024-08-02 by Kevin Feasel

Steven Sanderson builds a sample:

Stratified sampling is a technique used to ensure that different subgroups (strata) within a population are represented in a sample. This method is particularly useful when certain strata are underrepresented in a simple random sample. In this post, we’ll explore how to perform stratified sampling in R using both base R and the dplyr package. We’ll walk through examples and explain the code, so you can try these techniques on your own data.

Click through to see how.

Comments closed

AutoML in Python with TPOT

Published 2024-07-29 by Kevin Feasel

Abid Ali Awan gives us a primer on TPOT:

AutoML is a tool designed for both technical and non-technical experts. It simplifies the process of training machine learning models. All you have to do is provide it with the dataset, and in return, it will provide you with the best-performing model for your use case. You don’t have to code for long hours or experiment with various techniques; it will do everything on its own for you.

In this tutorial, we will learn about AutoML and TPOT, a Python AutoML tool for building machine learning pipelines. We will also learn to build a machine learning classifier, save the model, and use it for model inference.

Click through to see an example of how to use the library.

Comments closed

Changing Distributions and Simpson’s Paradox

Published 2024-07-25 by Kevin Feasel

Jerry Tuttle describes a paradox:

So you spent hours, or maybe days, cranking out thousands of numbers, you submit it to your boss just at the deadline, your boss quickly peruses your exhibit of numbers, points to a single number and says, “This number doesn’t look right.” Bosses have an uncanny ability to do this.

      Your boss is pointing to something like this: Your company sells property insurance on both personal and commercial properties. The average personal property premium increased 10% in 2024. The average commercial property premium increased 10% in 2024. But you say the combined average property premium decreased 3% in 2024. You realize that negative 3% does not look right.

Although the blog post doesn’t explicitly mention Simpson’s paradox, I’d argue that this is a good example of the idea. H/T R-Bloggers.

Comments closed

Using the fast_regression() Method in tidyAML

Published 2024-07-19 by Kevin Feasel

Steven Sanderson says, It’s my regression and I want it NOW:

If you’ve ever faced the daunting task of setting up multiple regression models in R, you’ll appreciate the convenience and efficiency that tidyAML brings to the table. Today, we’re diving into one of its standout functions: fast_regression(). This function is designed to streamline the regression modeling process, allowing you to quickly create and evaluate a variety of model specifications with minimal code.

Read on to see how the function works.

Comments closed

Transferring Linear Model Coefficients

Published 2024-07-15 by Kevin Feasel

Nina Zumel performs a swap:

A quick glance through the scikit-learn documentation on linear models, or the CRAN task view on Mixed, Multilevel, and Hierarchical Models in R reveals a number of different procedures for fitting models with linear structure. Each of these procedures meet different needs and constraints, and some of them can be computationally intensive to compute. But in the end, they all have the same underlying structure: outcome is modelled as a linear combination of input features.

But the existence of so many different algorithms, and their associated software, can obscure the fact that just because two models were fit differently, they don’t have to be run differently. The fitting implementation and the deployment implementation can be distinct. In this note, we’ll talk about transferring the coefficients of a linear model to a fresh model, without a full retraining.

I had a similar problem about 18 months ago, though much easier than the one Nina describes, as I did have access to the original data and simply needed to build a linear regression in Python that matched exactly the one they developed in R. Turns out that’s not as easy to do as you might think: the different languages have different default assumptions that make the results similar but not the same, and piecing all of this together took a bit of sleuthing.

Comments closed

A/B Testing with Survival Analysis in R

Published 2024-07-12 by Kevin Feasel

Iyar Lin combines two great flavors:

Usually when running an A/B test analysts assign users randomly to variants over time and measure conversion rate as the ratio between the number of conversions and the number of users in each variant. Users who just entered the test and those who are in the test for 2 weeks get the same weight.

This can be enough for cases where a conversion either happens or not within a short time frame after assignment to a variant (e.g. Finishing an on-boarding flow).

There are however many instances where conversions are spread over a longer time frame. One example would be first order after visiting a site landing page. Such conversions may happen within minutes, but a large churn could also happen within days after the first visit.

Read on for the scenario, as well as a simulation. I will note that, in the digital marketing industry, there’s usually a hard cap on number of days where you’re able to attribute a conversion to some action for exactly the reason Iyar mentions. H/T R-Bloggers.

Comments closed

Random Walks in R with TidyDensity

Published 2024-07-11 by Kevin Feasel

Steven Sanderson goes for a walk:

A random walk is a mathematical object that describes a path consisting of a succession of random steps. It’s a cornerstone concept in fields like physics, economics, and biology. In finance, for example, the random walk hypothesis suggests that stock market prices evolve according to a random walk and thus cannot be predicted.

Read on to see how you can generate a dataset matching a random walk, as well as a comparison of techniques for generating them.

Comments closed

Tips for Choosing a Classifier

Published 2024-07-03 by Kevin Feasel

I’ve wrapped up yet another series:

In this video, I wrap up the series on classification and provide some quick-and-dirty tips on when to use each of the classification algorithms we have discussed.

This was a series I really enjoyed. I’ve had a talk on the topic for a few years, but getting the opportunity to dig in deeper and spend a few hours on the topic was nice. It also helped me fill in some gaps in my understanding and fix a few long-standing bugs in my demo code, so it’s got that going for it as well.

Comments closed

SHAP and Additive Models

Published 2024-07-01 by Kevin Feasel

Michael Mayer answers a pair of related questions:

Within only a few years, SHAP (Shapley additive explanations) has emerged as the number 1 way to investigate black-box models. The basic idea is to decompose model predictions into additive contributions of the features in a fair way. Studying decompositions of many predictions allows to derive global properties of the model.

What happens if we apply SHAP algorithms to additive models? Why would this ever make sense?

Read on for the answers to these two questions.

Comments closed

Random Walks and Brownian Motion in healthyR.ts

Published 2024-06-28 by Kevin Feasel

Steven Sanderson goes for a walk on the stock exchange:

In the world of time series analysis, Random Walks, Brownian Motion, and Geometric Brownian Motion are fundamental concepts used in various fields, including finance, physics, and biology. Today, we’ll explore these concepts using functions from the healthyR.ts package.

Click through to learn about each of these concepts and some examples of how you can generate time series datasets following each of them.

Comments closed