The slider package provides support for flexible sliding window aggregation, and we can use these kinds of sliding windows to analyze rents over time.continue reading.
Author: Blog on Julia Silge
Use summarization, a single linear model, and bootstrapping to understand what economic activities involve a larger pay gap for women.continue reading.
The spatialsample package is gaining many new methods this summer, and we can use spatially aware resampling to understand how drought is related to other quantities across Texas.continue reading.
Understand how much money colleges spend on sports using linear modeling and bootstrap intervals.continue reading.
The tidymodels framework provides extension packages for specialized tasks such as Poisson regression. Learn how to fit a zero-inflated model for understanding how R package releases are related to number...continue reading.
The infer package is part of tidymodels and provides an expressive statistical grammar. Understand how to use infer, and celebrate Black History Month by learning more about the Tuskegee airmen.continue reading.
Get started with feature engineering for text data, transforming text to be used in machine learning algorithms.continue reading.
Learn how to train, explore, and understand an unsupervised topic model for text data.continue reading.
Using a tidymodels workflow can make many modeling tasks more convenient, but sometimes you want more flexibility and control of how to handle your modeling objects. Learn how to handle...continue reading.
Use spatial resampling to more accurately estimate model performance for geographic data.continue reading.
Get started with tidymodels workflowsets to handle and evaluate multiple preprocessing and modeling approaches simultaneously, using pumpkin competitions.continue reading.
Tune and evaluate a multiclass model with lasso regulariztion for economics working papers.continue reading.
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.continue reading.
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.continue reading.
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.continue reading.
More xgboost with tidymodels! Learn about feature engineering to incorporate text information as indicator variables for boosted trees.continue reading.
Our new book in the Chapman & Hall/CRC Data Science Series is now complete and available for preorder!continue reading.
Early stopping can keep an xgboost model from overfitting.continue reading.