tidymodels

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.

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.

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.

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

Early stopping can keep an xgboost model from overfitting.

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

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.

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.

Handling class imbalance in modeling affects classification metrics in different ways. Learn how to use tidymodels to subsample for class imbalance, and how to estimate model performance using resampling.

Tune a decision tree model to predict whether a Mario Kart world record used a shortcut, and explore partial dependence profiles for the world record times.