This week, Ellis and I add another classification model to our {tidymodels} series. Here, we walk through the framework of creating a Naive Bayes Classifier for classification purposes.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
If you like what we are doing with the screen cast and want to support our work and buy us a beer or coffee, check out our Patreon page!
This week, Ellis Hughes and I extend our {tidymodels} series by building a random forest classifier on the Palmer Penguins data set.
Some things we cover:
1. Continuing to refine our {tidymodels} frame work
2. Different approaches to setting up a tuning grid
3. Finalizing your workflow
4. Plotting ROC Curves for multi-class classification problems
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
This week, Ellis Hughes and I start exploring classification algorithms in {tidymodels}. We set up a logistic regression using NHL data to forecast whether a team will make the playoffs or not. Like all of our {tidymodels} episodes, we discuss:
To view our screen cast, CLICK HERE.
To access our code, CLICK HERE.
Also, if you enjoy our screen casts and find the useful, we have created a patreon page.
Ellis Hughes and I discuss how to tune decision trees for regression within the {tidymodels} framework. We cover:
* Pre-processing data
* Splitting data into training and test sets
* Setting tuning parameters and a tuning grid
* Fitting models and gathering model evaluation metrics
* Selecting the final model following tuning and fitting that model to the test data set
* Visualizing your outcomes
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
Continuing with our {tidymodels} tutorial, Ellis Hughes and I review how to create cross-validation folds when training your model and then fitting the model and collecting model metrics that allow you to compare different models and evaluate how well the model is predicting.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.