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Category: Data Science

Boosting versus Bagging in Tree Models

Published 2024-09-27 by Kevin Feasel

Vinod Chugani compares two techniques for working with trees:

Ensemble learning techniques primarily fall into two categories: bagging and boosting. Bagging improves stability and accuracy by aggregating independent predictions, whereas boosting sequentially corrects the errors of prior models, improving their performance with each iteration. This post begins our deep dive into boosting, starting with the Gradient Boosting Regressor. Through its application on the Ames Housing Dataset, we will demonstrate how boosting uniquely enhances models, setting the stage for exploring various boosting techniques in upcoming posts.

Read on for more information. The neat part about the “boosting versus bagging” debate is that both techniques are quite useful. Although boosting (via algorithms like XGBoost or LightGBM) is the more popular technique, bagging (random forest) is extremely powerful in its own right.

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Simple Outlier Detection and Removal in R

Published 2024-09-25 by Kevin Feasel

Steven Sanderson looks for oddities:

Outliers can significantly skew your data analysis results, leading to inaccurate conclusions. For R programmers, effectively identifying and removing outliers is crucial for maintaining data integrity. This guide will walk you through various methods to handle outliers in R, focusing on multiple columns, using a synthetic dataset for demonstration.

The techniques Steven uses are perfectly reasonable (though I like to use MAD from the median rather than standard deviations from the mean because MAD from the median doesn’t suffer from the sorts of endogeneity problems standard deviation does in a dynamic process). My primary warning would be to keep outliers in a dataset unless you know why you’re removing them. If you know the values were impossible or wrong—for example, a person who works 500 hours a week—that’s one thing. But sometimes, you get exceptional values out of an ordinary process, and those values are just as real as any other. I might have had a sequence in which I flipped a fair coin and it landed on heads 10 times in a row. It’s statistically very uncommon, but that doesn’t mean you can ignore it as a possibility or pretend it didn’t happen.

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Fun with the Cauchy Distribution

Published 2024-09-20 by Kevin Feasel

John Cook checks out fat tails:

Someone with no exposure to probability or statistics likely has an intuitive sense that averaging random variables reduces variance, though they wouldn’t state it in those terms. They might, for example, agree that the average of several test grades gives a better assessment of a student than a single test grade. But data from a Cauchy distribution doesn’t behave this way.

This is one of my favorite distributions to mess with, though I’m glad few things naturally follow a Lorentzian. I had a colleague of mine build an Excel spreadsheet to describe the behavior, showing that results never converge and you could easily pull values 50+ “standard deviations” from the “mean.” I use scare quotes there because neither concept actually exists for the distribution, but if you mistake it for an awkwardly thin normal distribution, it can lead to some interesting results.

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Natural Language Pre-Processing with Python

Published 2024-09-18 by Kevin Feasel

Harris Amjad does some text cleanup:

Natural Language Processing (NLP) is currently all the rage in the current machine learning landscape. With technologies like ChatGPT, Gemini, Llama, and so many other state-of-the-art text generators getting popular with the mainstream public, many newcomers are pouring into the field of NLP. Unfortunately, before we delve into how these fancy chatbots work, we must understand how we are engineering and treating our data before we feed it to our model. In this tip, we will introduce and implement some basic text preprocessing and cleaning techniques with Python.

Click through for some common operations. Some of these are very important for certain tasks but likely unhelpful for others. That could include things like lower-casing all words or removing stopwords. There are also some operations like spell checking and jargon expansion (or replacement) that you will likely want to include in a real-life project with actual people entering the data, versus a tidy sample dataset.

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Random Walks in R with RandomWalker

Published 2024-09-17 by Kevin Feasel

Steven Sanderson is going for a walk (not the after-dinner kind):

Welcome to the world of ‘RandomWalker’, an innovative R package designed to simplify the creation of various types of random walks. Developed by myself and my co-author, Antti Rask, this package is in its experimental phase but promises to be a powerful tool for statisticians, data scientists, and financial analysts alike. With a focus on Tidyverse compatibility, ‘RandomWalker’ aims to integrate seamlessly into your data analysis workflows, offering both automatic and customizable random walk generation.

Read on to learn more about the package, including why you might want to use it and the functionality you can get out of it.

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Simple Data Cleanup with Pandas

Published 2024-09-17 by Kevin Feasel

Ivan Palomares Carrascosa builds a process:

Few data science projects are exempt from the necessity of cleaning data. Data cleaning encompasses the initial steps of preparing data. Its specific purpose is that only the relevant and useful information underlying the data is retained, be it for its posterior analysis, to use as inputs to an AI or machine learning model, and so on. Unifying or converting data types, dealing with missing values, eliminating noisy values stemming from erroneous measurements, and removing duplicates are some examples of typical processes within the data cleaning stage.

As you might think, the more complex the data, the more intricate, tedious, and time-consuming the data cleaning can become, especially when implementing it manually.

Ivan handles some of the most common types of data clean work and shows a simple way of implementing these.

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Building a GitHub Codespace Configuration for Polyglot Notebooks

Published 2024-09-16 by Kevin Feasel

Matt Eland makes some recommendations:

In order to get Polyglot Notebooks to work with GitHub Codespaces, you’ll need to match the current requirements of the Polyglot Notebooks extension and its underlying .NET Interactive kernels.

This relies on two files in your .devcontainer directory:

  • Dockerfile which describes the Docker container the Codespace will run in
  • devcontainer.json which describes how the dev container is configured in terms of extensions and ports

Read on to learn more. Also, Matt has a brand new book available on the topic of polyglot notebooks, so check that out.

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The Importance of Versioning Data

Published 2024-09-12 by Kevin Feasel

John Mount demonstrates an important concept:

Our business goal is to build a model relating attendance to popcorn sales, which we will apply to future data in order to predict future popcorn sales. This allows us to plan staffing and purchasing, and also to predict snack bar revenue.

In the above example data, all dates in August of 2024 are “in the past” (available as training and test/validation data) and all dates in September of 2024 are “in the future” (dates we want to make predictions for). The movie attendance service we are subscribing to supplies

  • past schedules
  • past (recorded) attendance
  • future schedules, and
  • (estimated) future attendance.

John’s example scenario covers the problem of future estimations interfering with model quality. Another important scenario is when the past changes. As one example, digital marketing providers (think Google, Bing, Amazon, etc.) will provide you impression and click data pretty quickly, and each day they close the books on a prior day’s data at some normal time. For some of these providers, that prior day’s data is yesterday’s data—on Tuesday, provider X closes the books on Monday’s data and promises that it won’t change after that. But for other providers, they might change data over the course of the next 10 days. This means that the data you’re using for model training might change from under you, and you might never know if you don’t keep track of the actual data you used for training at the time of training.

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Dealing with Collinearity using Lasso Regression

Published 2024-09-10 by Kevin Feasel

Vinod Chugani always moves in the same direction:

One of the significant challenges statisticians and data scientists face is multicollinearity, particularly its most severe form, perfect multicollinearity. This issue often lurks undetected in large datasets with many features, potentially disguising itself and skewing the results of statistical models.

In this post, we explore the methods for detecting, addressing, and refining models affected by perfect multicollinearity. Through practical analysis and examples, we aim to equip you with the tools necessary to enhance your models’ robustness and interpretability, ensuring that they deliver reliable insights and accurate predictions.

Read on to learn a bit more about how collinearity works and how you can use lasso regression (instead of ridge regression) to deal with the problem.

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Sampling without Replacement and Unequal Probabilities

Published 2024-09-04 by Kevin Feasel

Peter Ellis finds interesting results with sampling in R:

A week ago I was surprised to read on Thomas Lumley’s Biased and Inefficient blog that when using R’s sample() function without replacement and with unequal probabilities of individual units being sampled:

“What R currently has is sequential sampling: if you give it a set of priorities w it will sample an element with probability proportional to w from the population, remove it from the population, then sample with probability proportional to w from the remaining elements, and so on. This is useful, but a lot of people don’t realise that the probability of element i being sampled is not proportional to w_i”

Read on for a demonstration. H/T R-Bloggers.

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