Which #TidyTuesday Netflix titles are movies and which are TV shows?
Use tidymodels to build features for modeling from Netflix description text, then fit and evaluate a support vector machine model.continue reading.
Use tidymodels to build features for modeling from Netflix description text, then fit and evaluate a support vector machine model.continue reading.
Use tidymodels to predict post office location with subword features and a support vector machine model.continue reading.
Explore country-level UN voting with a tidymodels approach to unsupervised machine learning.continue reading.
Estimate how commercial characteristics like humor and patriotic themes change with time using tidymodels functions for bootstrap confidence intervals.continue reading.
Use tidy data principles to understand which kinds of occupations are most similar in terms of demographic characteristics.continue reading.
Explore results of models with convenient tidymodels functions.continue reading.
I am happy to announce that this free, open source, interactive course on text mining with tidy data principles is now published!continue reading.
Check residuals and other model diagnostics for regression models trained on text features, all with tidymodels functions.continue reading.
Download up-to-date city data from Chicago’s open data portal and predict whether a traffic crash involved an injury with a bagged tree model.continue reading.
The current development version of tidytext has changes that may affect your analyses.continue reading.
Use tidymodels scaffolding functions for getting started quickly with commonly used models like random forests.continue reading.
Use tidymodels to predict capacity for Canadian wind turbines with decision trees.continue reading.
Which of the Datasaurus Dozen are easier or harder for a random forest model to identify? Learn how to use multiclass evaluation metrics to find out.continue reading.
Tune a hyperparameter and then understand how to choose the best value afterward, using tidymodels for modeling the relationship between expected wins and tournament seed.continue reading.
Use tidymodels for feature engineering steps like imputing missing data and subsampling for class imbalance, and build predictive models to predict the probability of survival for Himalayan climbers.continue reading.
An initial version of the first eleven chapters are available today! Look for more chapters to be released in the near future.continue reading.
Learn how to use tidyverse and tidymodels functions to fit and analyze many models at once.continue reading.
Use text features and tidymodels to predict the speaker of individual lines from the show, and learn how to compute model-agnostic variable importance for any kind of model.continue reading.
Build two kinds of classification models and evaluate them using resampling.continue reading.
Announcing our new book, to be published in the Chapman & Hall/CRC Data Science Series!continue reading.