Predict #TidyTuesday giant pumpkin weights with workflowsets

Get started with tidymodels workflowsets to handle and evaluate multiple preprocessing and modeling approaches simultaneously, using pumpkin competitions.

Multiclass predictive modeling for #TidyTuesday NBER papers

Tune and evaluate a multiclass model with lasso regulariztion for economics working papers.

Dimensionality reduction for #TidyTuesday Billboard Top 100 songs

Songs on the Billboard Top 100 have many audio features. We can use data preprocessing recipes to implement dimensionality reduction and understand how these features are related.

Fit and predict with tidymodels for #TidyTuesday bird baths in Australia

In this screencast, focus on some tidymodels basics such as how to put together feature engineering and a model algorithm, and how to fit and predict.

Modeling human/computer interactions on Star Trek from #TidyTuesday with workflowsets

Learn how to evaluate multiple feature engineering and modeling approaches with workflowsets, predicting whether a person or the computer spoke a line on Star Trek.

Predict housing prices in Austin TX with tidymodels and xgboost

More xgboost with tidymodels! Learn about feature engineering to incorporate text information as indicator variables for boosted trees.

Tune xgboost models with early stopping to predict shelter animal status

Early stopping can keep an xgboost model from overfitting.

Use racing methods to tune xgboost models and predict home runs

Models like xgboost have many tuning hyperparameters, but racing methods can help identify parameter combinations that are not performing well.

Predict which #TidyTuesday Scooby Doo monsters are REAL with a tuned decision tree model

Which Scooby Doo monsters are REAL?! Walk through how to tune and then choose a decision tree model, as well as how to visualize and evaluate the results.

Create a custom metric with tidymodels and NYC Airbnb prices

Predict prices for Airbnb listings in NYC with a data set from a recent episode of SLICED, with a focus on two specific aspects of this model analysis: creating a custom metric to evaluate the model and combining both tabular and unstructured text data in one model.