Extending ML for Text Classification you own this product

prerequisites
intermediate R • data splitting • feature engineering using hashes and word embeddings • fit models for multiclass outcomes • tune machine learning models
skills learned
resample datasets • generate feature hashes for categorical variables • implement pre-trained word embeddings in a machine learning workflow • subsample an unbalanced dataset • evaluate classification models • explain how a machine learning model generates specific predictions
Benjamin Soltoff
1 week · 4-6 hours per week · INTERMEDIATE
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Imagine you’re an academic researcher working on a project for predicting trends in the U.S. government’s policy-making priorities. Using modern techniques for text data feature engineering, you’ll fit a set of models, subsample the training data to minimize bias, evaluate the models’ performance using a test-set of observations, and leverage a tidy workflow to explain how a model generates specific predictions.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Benjamin Soltoff
Benjamin Soltoff is an assistant senior instructional professor in computational social science at the University of Chicago. He’s the associate director of the Masters in Computational Social Science program and teaches courses in research design, programming in R, data visualization, and machine learning. He holds a PhD in political science from Pennsylvania State University. He develops training workshops for learners in academia and industry on data science techniques using R with an emphasis on reproducible workflows, and he’s an RStudio-certified trainer. For more information, you can view his personal site.

prerequisites

This liveProject is for intermediate R programmers who know the basics of data science and have used the tidymodels framework for creating ML models. To begin these liveProjects you will need to be familiar with the following:

TOOLS
  • Intermediate R
TECHNIQUES
  • Data splitting
  • Feature engineering using hashes and word embeddings
  • Fit models for multiclass outcomes
  • Tune machine learning models

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