Learn

Analyze the results of correlation tests and simple regression models for many data sets at once.

Summarize clustering characteristics and estimate the best number of clusters for a data set.

Apply bootstrap resampling to estimate uncertainty in model parameters.

Perform common hypothesis tests for statistical inference using flexible functions.

Use tests of independence and goodness of fit to analyze tables of counts.

Create and train different kinds of regression models with different computational engines.

Train a classification model and evaluate its performance.

Improve model performance in imbalanced data sets through undersampling or oversampling.

Calculate performance estimates for time series forecasts using resampling.

Create models that use coefficients, extract them from fitted models, and visualize them.

Choose hyperparameters for a model by training on a grid of many possible parameter values.

Estimate the best hyperparameters for a model using nested resampling.

Identify the best hyperparameters for a model using Bayesian optimization of iterative search.

Prepare text data for predictive modeling and tune with both grid and iterative search.

Create models that use coefficients, extract them from fitted models, and visualize them.

Write a new recipe step for data preprocessing.

Create a parsnip model function from an existing model implementation.

Create a new performance metric and integrate it with yardstick functions.

Build functions to use in tuning both quantitative and qualitative parameters.

Write tidy(), glance(), and augment() methods for new model objects.