
Correlation and regression fundamentals with tidy data principles
Analyze the results of correlation tests and simple regression models for many data sets at once.

Kmeans clustering with tidy data principles
Summarize clustering characteristics and estimate the best number of clusters for a data set.

Bootstrap resampling and tidy regression models
Apply bootstrap resampling to estimate uncertainty in model parameters.

Hypothesis testing using resampling and tidy data
Perform common hypothesis tests for statistical inference using flexible functions.

Statistical analysis of contingency tables
Use tests of independence and goodness of fit to analyze tables of counts.
Learn
After you know what you need to get started with tidymodels, you can learn more and go further. Find articles here to help you solve specific problems using the tidymodels framework. Articles are organized into four categories:

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

Classification models using a neural network
Train a classification model and evaluate its performance.

Subsampling for class imbalances
Improve model performance in imbalanced data sets through undersampling or oversampling.

Modeling time series with tidy resampling
Calculate performance estimates for time series forecasts using resampling.

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

Model tuning via grid search
Choose hyperparameters for a model by training on a grid of many possible parameter values.

Nested resampling
Estimate the best hyperparameters for a model using nested resampling.

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

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

Creating case weights based on time
Create models that use coefficients, extract them from fitted models, and visualize them.

Create your own recipe step function
Write a new recipe step for data preprocessing.

How to build a parsnip model
Create a parsnip model function from an existing model implementation.

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

How to create a tuning parameter function
Build functions to use in tuning both quantitative and qualitative parameters.

Create your own broom tidier methods
Write tidy(), glance(), and augment() methods for new model objects.
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