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YouTube

Tuning Random Forest Hyperparameters with Tidymodels

Julia Silge via YouTube

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Overview

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This course demonstrates hyperparameter tuning for a random forest classification model using tidymodels and tree data from San Francisco. It covers preprocessing, tuning grids, model evaluation, variable importance, and finalizing the model for predictions on new data.

Syllabus

Introduction
Data
Species
Date variable
Map
Village Visualization
Building a model
Preprocessing
Stepother
Results
Data Preprocessing
Date Column
Downsample
Model specification
Tuning
Workflow
Preprocessor
Parallel processing
Tuning results
Plotting
Tuning parameters
Updated grid
Regular grid
Transparent grid
Finding best values
Finalizing model spec
Adding the final model
Global variable importance
Testing data
Final workflow
Last fit

Taught by

Julia Silge

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