ML for Text Classification

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prerequisites
intermediate R • data splitting • feature engineering • fitting models for multiclass outcomes • tuning machine learning models
skills learned
resample datasets for unbiased measures of model performance • feature engineering for text data • fit models using a tidy framework • evaluate classification models using appropriate metrics • tune machine learning models to maximize their effectiveness
Benjamin Soltoff
1 week · 4-6 hours per week · INTERMEDIATE
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Play the role of an academic researcher preparing a machine learning model to predict the U.S. government’s focus for new policy legislation. You’ll process the legislation dataset with resampling and feature engineering techniques, employ a range of algorithms, including penalized regression and XGBoost, to fit a series of ML models, evaluate the effectiveness of the models, and tune them accordingly.

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. To begin these liveProjects you will need to be familiar with the following:

TOOLS
  • Intermediate R
TECHNIQUES
  • Data splitting
  • Feature engineering
  • Fitting models for multiclass outcomes
  • Tuning machine learning models

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