TidyX

Hosts

Ellis Hughes and Patrick Ward.

Ellis has been working with R since 2015 and has a background working as a statistical programmer in support of both Statistical Genetics and HIV Vaccines. He also runs the Seattle UseR Group.

Patrick's current work centers on research and development in professional sport with an emphasis on data analysis in American football. Previously, He was a sport scientist within the Nike Sports Research Lab. Research interests include training and competition analysis as they apply to athlete health, injury, and performance.

Description

The goal of TidyX is to explain how R code works. We are focusing on submissions to the #TidyTuesday Project to help promote the great work being done there.

In this repository, you will find copies of the code we've explained, and the code we wrote to show the concept on a new dataset.

To submit code for review, email us at [email protected]

To watch more episodes, go to our youtube channel.

Patreon

If you appreciate what we are doing and would like to support TidyX, please consider signing up to be a patron through Patreon.

https://www.patreon.com/Tidy_Explained

TidyX Episodes

• Episode 1: Introduction and Treemaps!

  • UseR Highlighted: Courtney Gerver
  • Original Tweet
  • Source Code

• Episode 2: The Office, Sentiment, and Wine

  • UseR Highlighted: Robin Sifre
  • Original Tweet
  • Source Code

• Episode 3: TBI, Polar Plots and the NBA

  • UseR Highlighted: Raniere Silva
  • Original Tweet
  • Source Code

• Episode 4: A New Hope, {Patchwork} and Interactive Plots

  • UseR Highlighted: Maggie Sogin
  • Original Tweet
  • Source Code

• Episode 5: Tour de France and {gganimate}

  • UseR Highlighted: Owen Churches
  • Original Tweet
  • Source Code

• Episode 6: Lollipop Charts

  • UseR Highlighted: Priya Shukla
  • Original Tweet
  • Source Code

• Episode 7: GDPR Faceting

  • UseR Highlighted: Danielle Barnas
  • Original Tweet
  • Source Code

• Episode 8: Broadway Line Tracing

  • UseR Highlighted: Jake Kaupp
  • Original Tweet
  • Source Code

• Episode 9: Tables and Animal Crossing

  • UseR Highlighted: Ted Lederas
  • Original Tweet
  • Source Code

• Episode 10: Volcanoes and Plotly

  • Ellis and Patrick explore this weeks TidyTuesday Dataset!

• Episode 11: Times Series and Bayes

  • UseR Highlighted: Eric Ekholm
  • Original Tweet
  • Source Code

• Episode 12: Cocktails with Thomas Mock

  • UseR Highlighted: Joshua de la Bruere
  • Original Tweet
  • Source Code

• Episode 13: Marble Races and Bump Plots

  • UseR Highlighted: Cédric Scherer
  • Original Tweet
  • Source Code

• Episode 14: African American Achievements

  • UseR Highlighted: Catriona Cunningham
  • Original Tweet
  • Source Code

• Episode 15: Juneteenth and Census Tables

  • Ellis and Patrick show US Census tables in a report, broken down into divisions and highlight values using {colortable}
  • Source Code

• Episode 16: Caribou Migrations and NBA Shots on Basket

  • UseR Highlighted: Jihong Zhang
  • Original Tweet
  • [Source Code](https://github.com/thebioengineer/TidyX/blob/master/TidyTuesday_Explained/016-Caribou_Migrations_and_Spatial_Analysis/Jihong Zhang - Caribou Migration Map.Rmd)

• Episode 17: Uncanny X-men and Feature Engineering

  • UseR Highlighted: Rebecca Stevick
  • Original Tweet
  • Source Code

• Episode 18: Coffee and Random Forest

  • UseR Highlighted: Nyssa Silbiger
  • Original Tweet
  • Source Code

• Episode 19: Astronauts and Dashboards

  • UseR Highlighted: Lauren Pandori
  • Original Tweet
  • Source Code

• Episode 20: Cocktails with David Robinson

  • UseR Highlighted: David Robinson
  • Original Tweet
  • Source Code

• Episode 21: The Birds

  • UseR Highlighted: Roman Link
  • Original Tweet
  • Source Code

• Episode 22: European Energy and Ball Hogs

  • UseR Highlighted: Kelly Cotton
  • Original Tweet
  • Source Code

• Episode 23: Mailbag and Expected Wins

  • Ellis and Patrick go into our mailbag and focus on a request we recently had on loops and functions.
  • Source Code

• Episode 24: Waffle plots and Shiny

  • UseR Highlighted: Jared Braggins
  • Original Tweet
  • Source Code

• Episode 25: Intro To Shiny

  • This is a start of a series of episodes covering more in-depth uses for {Shiny}, an R package for creating web applications by Joe Cheng. In this episode we cover basics of Shiny, and explain the concept of reactive programming.
  • Source Code

• Episode 26: Labels and ShinyCARMELO - Part 1

  • UseR Highlighted: Mr. Ochiwar
  • Original Tweet
  • Source Code

• Episode 27: LIX and ShinyCARMELO - Part 2

  • UseR Highlighted: Leon Jessen
  • Original Tweet
  • Source Code

• Episode 28: Nearest Neighbors and ReactiveValues

  • This week Ellis and Patrick explore how to perform career analysis and projections using the KNN algorithm.Using those concepts, we jump into part three of our shiny demo series where we have shiny execute a KNN for our input players. We show how to create an action button to execute our code, and reactiveValues to store the results to then plot!
  • Source Code

• Episode 29: Palettes and Random Effects

  • UseR Highlighted: Kaylea Haynes
  • Original Tweet
  • Source Code

• Episode 30: Tweet Sentiment

  • Patrick and Ellis were inspired this week by all the sentiment analysis performed for #TidyTuesday this week so we decided to look at tweets to show and comment on additional things to be aware of when doing sentiment analysis. Using {rtweet}, we pull over 50,000 tweets that used the #Debate2020, and discuss how context is incredibly important to analysis.
  • Source Code

• Episode 31: Reactable

  • This weeks #TidyTuesday dataset was on NCAA Womens Basketball Tournament appearances. Patrick and Ellis in the past have shown how tables can be used for data visualization, and wanted to learn more about another one. {reactable} is a really cool looking package, so we spend some time showing how to use the package, apply column definitions, and even apply html widgets within the table!
  • Source Code

• Episode 32: Shiny with Eric Nantz

  • This weeks #TidyTuesday dataset was a super fun one. Ellis and Patrick are joined by Eric Nantz, who created a shiny app to explore and animate the data. We talk through several new shiny concepts, like using {golem}, cross-talk, and other shiny packages like {bs4dash}!

  • UseR Highlighted: Eric Nantz

  • Source Code

• Episode 33: Beer and State Maps

  • UseR Highlighted: Richard Bamattre
  • Original Tweet
  • Tweet Source Code
  • TidyX Source Code

• Episode 34: Wind and Maps

  • UseR Highlighted: Florence V. Dubois
  • Original Tweet
  • Tweet Source Code
  • TidyX Source Code

• Episode 35: Rectangles

  • UseR Highlighted: Henry Wakefield
  • Original Tweet
  • Tweet Source Code
  • TidyX Source Code

• Episode 36: Animated Plotly

  • This weeks #TidyTuesday dataset was on Mobile and Landline subscriptions across the world. This week we saw lots of animation type plots, and wanted to add our own. Using {plotly}, we make an interactive plot that animates across time to show how GDP is related to the raw subscription numbers. We also do some exploration with line plots.
  • Source Code

• Episode 37: Code Review

  • Looking back at ones code can show you just how far you have come. Sparked by a conversation between Ben Baldwin (@benbaldwin), Patrick and Ellis, this weeks episode is on code review and refactoring. Ben went into his past and has furnished a set of code for us to try to refactor. In the spirit of things, neither of us looked closely at the code ahead of time, and recorded our initial reactions and process of refactoring Bens code into a function that could be applied to multiple datasets!
  • UseR Highlighted: Ben Baldwin
  • Original Tweet
  • Tweet Source Code
  • TidyX Source Code

• Episode 38: Polar Plots

  • UseR Highlighted: Tobias Stalder
  • Original Tweet
  • Tweet Source Code

• Episode 39: Imputing Missingness

  • This weeks we reach into our mailbag to answer a request from Eric Fletcher(@iamericfletcher) on imputing NA's. In this video we scrape 2013 draft data, and impute using various techniques missing times for the three cone event. We also attempt to discuss Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR) - but we decide at the end to leave it to the professionals.
  • Source Code

• Episode 40: Inspiring Women and Plotly

  • UseR Highlighted: Jackie Torres
  • Original Tweet
  • Tweet Source Code
  • TidyX Source Code

• Episode 41: Worm Charts with Alice Sweeting

  • Alice Sweeting(@alicesweeting) joins us as a guest explainer this week! We are very excited to have her on as she explains with us how she worked through creating a worm chart of a super netball game! She talks with us on common techniques she uses to process data, mixing base R with tidyverse. Then we spend some time discussing Alice's background, current role, and advice for folks looking to get started in sports analytics or R programming in general.

  • UseR Highlighted: Alice Sweeting

  • Source Code

• Episode 42: Highlighting Lines

  • UseR Highlighted: Peter
  • Original Tweet
  • Tweet Source Code
  • TidyX Source Code

• Episode 43: Funnel Plots, Plotly, and Hockey

  • With no #TidyTuesday dataset this week, we decide to continue to work through our learning of plotly. This time, using a tool known as a funnel plot.
  • Source Code

• Episode 44: Transit Costs, steps, and Plotly Maps

  • UseR Highlighted: Martin Devaux
  • Original Tweet
  • Blog Post
  • TidyX Source Code

• Episode 45: NHL Pythagorean Wins and Regression

  • This week we reflect back on the past year and combine techniques from multiple episodes. We scrape multiuple tables from the the hockey reference website, use regular expressions to clean and organize the data, and use for loops to determine the optimal pythagorean win exponent. We visualize the data using several different techniques, like scatter and lollipop charts. We show some fun tools with regularizing values for linear regressions and how how to predict and visualize the results.
  • Source Code

• Episode 46: Circle Plots, NHL Salaries, and Logistic Regression

  • UseR Highlighted: Natalie O'Shea
  • Original Tweet
  • Tweet Source Code
  • TidyX Source Code

• Episode 47: NHL Win Probabilities and GT Tables

  • This week we play with a new technique for optimizing, the optim function! We scrape the 2019-2020 NHL season to generate power rankings for every NHL team and home-ice-edge. We can use this to then predict team winning probability! We then combine that with season summary data to generate a pretty GT table!
  • Source Code

• Episode 48: NBA Point Simulations

  • In this episode we show how to scrape the current NBA seasons scores to then generate a simple game simulator. Using {purrr} with some base R functions we generate outputs and show how to simulate thousands of games to generate outcome predictions.
  • Source Code

• Episode 49: MLB Batting Simulations

  • We continue looking at simulations this week, but this time for individual players. Using {Lahman}, we pull the 2019 MLB player batting stats, and visualize the stats using histograms and density plots. Next, to generate confidence intervals around their batting averages we use rbinom() combined with techniques from the {tidyverse} to make simulation easy. Finally we visualize the data using {gt} combined with {sparklines}.
  • Source Code

• Episode 50: MLB Batting Simulations

  • Another MLB Batting episode. This time we use the James Stein Estimator (paper below) to apply a shrinkage estimate to player batting averages to get a "true" estimate, removing luck. Using {Lahman}, we pull the 2018 MLB player batting stats, and explain how to implement the estimator. Next, we compare estimates against the 2019 season. Finally we visualize the data using {gt}, using header spans, and cell styling. For the grand finale we combine this gt table with batting averages with plots using patchwork!.
  • Source Code

• Episode 51: Deploying Models with Shiny

  • Sharing the results of a modeling effort is an important skill of any data scientist. However, just sharing the weights of each predictory is often not good enough to get buy in from stakeholders who are understandably skeptical of your results. Using the power of shiny, you can show your stakeholders exactly how your model interprets and then predicts the results. In this episode, we use the {palmerpenguins} package with {randomforest} to generate a model to predict the species of a new penguin. With shiny, we then deploy our model to allow the users to record new penguins attributes to see whether the model things they are an Adelie, Chinstrap, or Gentoo! The output is a boxplot indicating the models probablity for each species given the inputs.
  • Source Code

• Episode 52: Too Many Gentoo with Xaringan

  • There are too many Gentoo, your PI proclaims. This weeks episode Patrick and Ellis talk how to use the {xaringan} package to produce reproducible html presentations using Rmarkdown syntax. We discuss how we looked at "raw" tech data and used summary statistics to compare against the gold standard {palmerpenguins} package from Dr. Allison Horst, Dr. Alison Hill, Data from Dr. Kristen Gorman. We use last weeks highly powerful machine learning model to generate presictions of species, and generate a confusion matrix of our data vs the predictions. Finally, we talk about the value of making your presentation based on Rmd and being able to update the presentation at the click of a button.
  • Source Code

• Episode 53: MLB Pitch Classification Introduction

  • This week we start a series on using machine learning to automate pitch classification. In this first episode, we discuss ways to start looking at your data and questions to formulate. We use hierarchical clustering a few different ways to start to see relationships between the different pitch types and the statistics that were captured around each pitch!
  • Source Code

• Episode 54: MLB Pitch Classification 2 - KNN, Caret and UMAP

  • In the second episode on using machine learning to automate pitch classification from pitchfx data, we apply the K-nearest-neighbors algorithm as our first attempt at classification. We start with using the results from our naive hierarchical clustering to select 4 groups and apply the KNN algorithm. We then look at how we could evaluate performance of the model both with total mis-classification and within class mis-classification. Then we use {carat} to optimize for the best clustering and compare the results. Finally, we use UMAP to perform dimensional reduction to visualize mulitple dimensions as two and view relationships within the clusters.
  • Source Code

• Episode 55: MLB Pitch Classification 3 - Decision Trees, Random Forests, optimization

  • For the third episode in the series on using machine learning to automate pitch classification from pitchfx data, we talk about decision trees and its famous variant: random forests. We start by discussing what a decision tree is and its value. We visualize the results and discuss the quality of the fit. Then we expand on decision trees, using the Random Forest algorithm, and discuss its performance. Finally, we use {caret} and {doParallel} to do a grid search for optimal mtry, using parallel processes to speed up the search!.
  • Source Code

• Episode 56: MLB Pitch Classification 4 - XGBoost

  • We now turn to the famous XGBoost algorithm to help us in our fourth episode in the series on using machine learning to automate pitch classification from pitchfx data. We start by training using default parameters and observe some tricks to make training faster. Then we use {caret} and {doParallel} to do a grid search for optimal settings to be using to train and discuss the merits and disadvantages of using ever more complicated ML models.
  • Source Code

• Episode 57: MLB Pitch Classification 5 - Naive Bayes Classification

  • We naively turn to bayes...okay, I'm done. In this episode we use the Niave Bayes Classifier from the {e1071} package to classify pitches from our pitchfx data. We discuss briefly how this algorithm works, and review its performance against the other tree-based algorithms we've used so far.
  • Source Code

• Episode 58: MLB Pitch Classification 6 - TensorFlow

  • The next model type is one that has had a lot of excitement over the last decade with the promise of "AI" - deep learning. Using the {keras} package from RStudio, we attempt to train a model to automate pitch classification from pitchfx data. We talk about the differences to consider when building a deep learning algorithm, and data prep that must be done. We finally review the restuls and talk a bit about black-box ML models.
  • Source Code

• Episode 59: MLB Pitch Classification 7 - Class Imbalance and Model Evaluation Intro

  • Throughout this series, we been attempting to predict pitch type using PitchF/X data. However, we have not directly addressed a major flaw in our data, class imbalance. The Four-seam FastBall consists of nearly 37% of our data! In this episode we apply a couple techniuqes to help address the class imbalance, and look at ways to evaluate our models performance. We talk about the pros and cons to consider, and set up for our last episode for the series.
  • Source Code

• Episode 60: MLB Pitch Classification 8 - Model Evaluation and Visualization

  • This week we apply everything we have learned over the last several weeks to attempt to pick the best model for our project. As a reminder, we are attempting to predict pitch type using a subset of PitchF/X data. We attempt to productionalize our evaluations by writing a series of functions that allow quick iteration across multiple input types and capturing of information. Finally, we visualize the evaluations using 2 gt tables. Thank you all so much for joining us for this mini series on ML models and being with us as we hit episode 60. This has been a wonderful ride!
  • Source Code

• Episode 61: Data Cleaning - Regular Expressions

  • Okay, we've gotta say it - there is nothing "regular" about regular expressions. BUT that does not mean they are not an incredibly valuable tool in your programming toolbox. In this episode we go through how to apply regular expressions to a dataset and talk through some of the common tokens you might use when applying a regular expression to your dataset.
  • Source Code

• Episode 62: Data Cleaning - REGEX applied & stringr

  • This week we continue using Regex, and this time talk about applying it to generate data for plots. Additionally we discuss techniques such as grouping, and using the stringr package for its str_* variants of the base R regex functions.
  • Source Code

• Episode 63: Data Cleaning - REGEX lookarounds & Player Gantt Charts

  • We lookaround with regex this week, showing an alternative approach to setting anchors in your regular expressions using lookarounds. We apply this to extracting player substitutions. Then we calculate the number of stints and duration for players to create a player gantt chart across the Miami Heat and Milwaukee Bucks Game 2 of the Eastern Conference Playoffs.
  • Source Code

• Episode 64: Data Cleaning - Ugly Excel Files Part 1

  • Ugly data. Ugly EXCEL data. Thats pretty common to come across as a data scientist. People unfamiliar with how to format data are often the ones creating the excel files you work with. This week, Patrick and Ellis talk through some techniques to handle these data and turn it into usable data. Patrick wrote up this weeks example, parsing through the data to generate a nice data.frame from the ugly excel example.
  • Source Code

• Episode 65: Data Cleaning - Ugly Excel Files Part 2

  • This week Ellis works through the ugly excel file, writing out the code live as he goes, and explaining how to break up the parsing into nice, bite-size pieces and generalize them. Patrick is there asking questions and clarifying how things worked. At the end of the cast they end up with similar data.frames, ready to munge for final processing.
  • Source Code

• Episode 66: Data Cleaning - Ugly Excel Files Part 3

  • Now that we have the excel file into a nice format, we go over the final pieces of processing to turn the incorrectly formatted fields into usable data. We talk about generating date objects, ifelse vs if_else, and have some fun!
  • Source Code

• Episode 67: Data Cleaning - Viewer Submitted Excel File

  • For the first time in over a year, and 65 episodes, Ellis and Patrick are in the same room! This week they work on a viewer-submitted excel file. After last weeks episode, we put out a call to our viewers to submit the ugly data they see so we can try to help. Github user MikePrt submitted a file from the UK Government statistics organisation (Office for National Statistics (ONS)) as an example. We extract the data and produce a simple plot.
  • Source Code

• Episode 68: Data Cleaning - Ugly Excel Files Part 4 - Saving Outputs

  • We continue our series on data cleaning and discuss sharing your outputs. Patrick and Ellis go over a few output file formats and two different excel libraries that give you differing levels of control over the outputs.
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• Episode 69: Modern Pentathlons with Mara Averick

  • Ellis and Patrick are joined today by Mara Averick, a Developer Advocate for RStudio. We conclude our series on messy excel data by talking through cleaning an excel file from UIPM and reasoning out what the field and scoring are.Then we talk about Mara's role, career history, and advice she has for our viewers.
  • UseR Highlighted: Mara Averick
  • Source Code

• Episode 70: Databases with {dplyr}

  • Making friends with your friendly database administrator is a great way to improve your effectiveness as a data scientist in your organization. But what do you do if you don't know any SQL? We present {dbplyr} by the folks at RStudio. Easily connect, interact with and send queries to databases using familiar dplyr syntax and commands.
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• Episode 71: Databases in R | Exploring Your Database with NBA data

  • Being handed a database without knowing its contents or where to start can be daunting. We talk about techniques we can use to start exploring it just like any other dataset. We get a list of the tables in your database, the column names, and show how you can write SQL to get the head of a table.
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• Episode 72: Databases in R | Shiny and Databases

  • The fastest way for a data scientist to multiply their impact is to get their customers to be able to do the analysis themselves (with guiderails of course). Shiny provides a great user interface, combing this with some basic queries your clients may want improves response time and allows them to search to their hearts content. This week we show you a simple way to add interactivity with your database using {shiny} to query teams mean point differential at home across the 2001-2002 seasons.
  • Source Code

• Episode 73: Databases in R | Shiny,Databases, and Reactive Polling

  • Now that we have a shiny app that allows our users to access and interact with the data in our database, how do we make sure that the user configuration is showing the most up-to-date information for selection? This is done through reactive polling - a time out feature that checks to see if there are any update to the database and updates the UI selection interface accordingly. We discuss the benefits and how to use the reactivePoll function combined with an observeEvent function to really supercharge our shiny app!
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• Episode 74: Databases with R | Joins in SQL vs Local

  • Continuing the SQL/Database saga, we look at joins. we scrape a bunch of play by play information and game info and look at generating a database with this information. We then compare the speed of joining tables locally or within the sql database!
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• Episode 75: Databases with R | J Joins, databases, and commits in Shiny

  • Now that we have a database full of data, and a shiny app to play with it, how do we capture and share the information across our users using the database? In this episode we share how we might create a sample database filled with play-by-play NBA data and create a shiny app to allow a coach or SME to review and add comments to the data as they review it. Then, they can decide to commit and save their thoughts for the future!
  • Source Code

• Episode 76: Databases with R | Polling databases in Shiny

  • In Episode 75 we introduced the idea of committing changes from a shiny app to a database. But what about scenarios with multiple users? Ellis and Patrick explore an idea to allow for polling of the database and add updates that were committed to the database to active views of the rest of the users. We use reactive polling as introduced in episode 73 and updating reactiveValues.
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• Episode 77: Tidymodels - LM

  • tidymodels is an ecosystem of packages developed by RStudio (Max Kuhn, Julia Silge to name a few) that is developed to help folks apply good modeling practices from start of the cleaned data to a fully productionalized model. We are going to be stepping through and learning how to apply tidymodels together. The first episode is on applying a simple linear model versus the base R method!
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• Episode 78: Tidymodels - Splits and Recipes

  • tidymodels is an ecosystem of packages developed by RStudio (Max Kuhn, Julia Silge to name a few) that is developed to help folks apply good modeling practices from start of the cleaned data to a fully productionalized model. We are going to be stepping through and learning how to apply tidymodels together. The second episode we discuss how to set up your test/train splits as well as data preprocessing using the {recipes} package in conjuction with {workflow}! This smooths out and applies good practices simply and effectively to make data prep for modeling a breeze.
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• Episode 79: Tidymodels - Cross-validation and Metrics

  • The third episode on tidymodels, we continue our data prep and model training by exploring cross-validation and metric evalidation. Ellis and Patrick show to set up a 5-fold cross validation set on your training split as well as fitting a tidymodels workflow! We finally show how to display and extract model fitting evaluation metrics.
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• Episode 80: Tidymodels - Decision Trees and Tuning

  • The fourth episode on tidymodels, we sort out how to do parameter tuning of a model using the tune package. We set up a grid to train across and select the best model based on model metrics. We then retrain this model on the full test set and evaluate its performance against the final test set.
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• Episode 81: Tidymodels - Logistic Regression with GLM

  • This week we look at how to perform a logistic regression using the tidymodels framework. During the fifth episode tidymodels, we show how to set up a logistic regression using GLM, perform a custom test/train split on the data, and calculate metrics such as ROC AUC, kappa, and accuracy. We visualize the performance and evaulate how well our model performed.
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• Episode 82: Tidymodels - Logistic Regression with GLM

  • Continuing looking at classification models via tidymodels, this week we look at how to perform a multiple classification problem using random forests. We show how to tune your model, extract the optimal workflow, and then train it against your full training set and compare its performance on the test set. We calculate performance metrics such as ROC AUC and visualize the results.
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• Episode 83: Tidymodels - Naive Bayes of Penguins

  • Naive bayes is the model we apply in this weeks Tidymodels series. We look at how to perform a multiple classification problem using the naive bayes theorem applied in the discrim package from tidymodels, and the klaR package to supply the engine. We show how to evaluate your model using 5-fold cross valudation, and then train it against your full training set and compare its performance on the test set. We calculate performance metrics such as ROC AUC and visualize the results.
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• Episode 84: Tidymodels - Workflow Sets and model selection

  • Tidymodels makes it simple to try a multitude of modeling types by separating the preprocessing from the model type and creating a standardized way to apply different models. Workflow sets takes this a step further and makes it so you can train and compare these models at the same time, just like tuning. Using data from Kaggle, we look at how to perform model fitting for three model types and select the best workflow to train using our full train set and compare against our hold out test set. We calculate performance metrics such as RMSE and R-squared and visualize the results.
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• Episode 85: Tidymodels - Tuning Workflow Sets

  • Tidymodels makes it simple to try a multitude of modeling types by separating the preprocessing from the model type and creating a standardized way to apply different models. In this episode we show how you can use Workflowsets along with tuning to create optimal models. Using wine data from Kaggle, we look at two different recipes and 3 different models requiring different levels of tuning. We select the best workflow and optimal tuned paramets to train using our full train set and compare against our hold out test set. We calculate performance metrics such as RMSE and R-squared and visualize the results.
  • Source Code