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Explore Changes in Art Over Time With Tidymodels

Julia Silge via YouTube

Overview

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This course demonstrates how to use tidymodels to train a regularized regression model with short text features from the Tate art collection. It covers sparse preprocessing, penalty tuning, variable importance, predictions, and residual diagnostics.

Syllabus

Introduction
Data set overview
The medium column
The artwork column
The distribution over time
Residuals
Biases
Materials
Preprocessing Data
Training Data
Token Filter
Transform to Matrix
Feature Preprocessing
Sparse Data
Change Range
Training
Results
RMSE
Penalty
Variable importance
Arranging by importance
Making a graph
Scales
Collect predictions
Collect predictions on art final
Filter predictions
Conclusion

Taught by

Julia Silge

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