Tidymodels: 100 Tips and Strategies for Efficient Machine Learning Workflows

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The tidymodels ecosystem in R provides a consistent and modular framework for modeling and machine learning. Here are 100 tips for working with tidymodels:

1. Installation and Loading:

1. Install tidymodels with install.packages("tidymodels").
2. Load the core tidymodels packages with library(tidymodels).

2. Create a Simple Model:

1. Create a linear regression model with lm() or a decision tree with decision_tree().
2. Use the formula interface for specifying the model formula.

3. Data Splitting:

1. Split your data into training and testing sets with initial_split() and training()/testing().
2. Utilize rsample functions for flexible data splitting.

4. Preprocessing:

1. Preprocess data with recipes using functions like step_center(), step_scale().
2. Create a recipe with recipe() and bake it with bake().

5. Model Specification:

1. Specify a model using linear_reg() for linear regression or decision_tree() for a decision tree.