tidymodels

Biostat 203B

1 Overview

• A typical data science project:

• tidymodels is an ecosystem for:

  1. Build and fit a model;
  2. Feature engineering: coding qualitative predictors, transformation of predictors (e.g., log), extracting key features from raw variables (e.g., getting the day of the week out of a date variable), interaction terms, …;
  3. Evaluate model using resampling (such as cross-validation).
     
  4. Tuning model parameters.

2 Heart data example

We illustrate a binary classification example using a dataset from the Cleveland Clinic Foundation for Heart Disease.

2.1 Logistic regression (with enet regularization) workflow

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2.2 Random forest workflow

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2.3 Boosting (XGBoost) workflow

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2.4 SVM (with radial basis kernel) workflow

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2.5 Multi-layer perceptron (MLP) workflow

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