Choose hyperparameters for a model by training on a grid of many possible parameter values. Read more »
Create and train different kinds of regression models with different computational engines. Read more »
Train a classification model and evaluate its performance. Read more »
Create a parsnip model function from an existing model implementation. Read more »
Estimate the best hyperparameters for a model using nested resampling. Read more »
Identify the best hyperparameters for a model using Bayesian optimization of iterative search. Read more »
Improve model performance in imbalanced data sets through undersampling or oversampling. Read more »
Prepare text data for predictive modeling and tune with both grid and iterative search. Read more »
Create models that use coefficients, extract them from fitted models, and visualize them. Read more »
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