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Build Features for Machine Learning from Netflix Description Text

Julia Silge via YouTube

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Overview

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This screencast demonstrates how to use tidymodels and tidy text features to train a linear support vector machine that distinguishes Netflix movies from TV shows based on descriptions. It covers preprocessing, class upsampling, cross-validation, evaluation, and interpreting predictive words.

Syllabus

Introduction
Exploring the data
Data Budget
Feature Engineering
Moat
Model calibration
The model
Resamples
Evaluation
Results
Confusion Matrix
Tidy
Autoplot
Collect metrics
Collect predictions
Consistent results
Tibble
Fitted workflow
Linear SVM
Top 15 words
Sign value
Plot
Testing
Visualization
Summary

Taught by

Julia Silge

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