Glossary

Key Points

Introduction	`tidymodels` is a collection of packages that are designed to work together to make it easier to build models.
Machine Learning Basics	Testing sets are used to estimate a models performance on new data Validation sets are used to choose between different models or hyperparameters Cross validation is a more advanced method for estimating the performance of a model Accuracy is a simple metric for evaluating a model Confusion matrices are a more detailed way of evaluating a model Precision and recall are useful metrics for evaluating models when the classes are imbalanced Bias and variance are useful concepts for understanding how a model will perform on new data
Data Preparation	You can use `rsample` to split your data into training and testing sets. You can use `recipes` to create a recipe to preprocess your data.
Building a Simple Workflow	A workflow is a series of steps that are executed in order to accomplish a task.
Hyperparameter Tuning	Hyperparameters are the settings of a model that we have to set ourselves.
After Training a Model	We can use `last_fit()` to test a model on the testing data. We can use `saveRDS()` and `readRDS()` to save and load a model.
Full Walkthrough For Classification	We can apply what we have learnt to a new dataset.

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