Machine Learning for Tabular Data in R: Glossary

There are many types of machine learning.

We will focus on some methods that work well with tabular data.

Classical linear and logistic regression models can be thought of as examples of regression and classification models in machine learning.

Testing sets can be used to measure the performance of a model.

Training data can give us a decision tree model.

Decision trees can be used for supervised learning, but they are not very robust.

Random forests can make predictions of a categorical or quantitative variable.

Random forests, with their default settings, work reasonably well.

Gradient boosted trees can be used for the same types of problems that random forests can solve.

The learning rate can affect the performance of a machine learning algorithm.

Parameter tuning can improve the fit of an XGBoost model.

Cross validation allows us to tune parameters using the training set only, saving the testing set for final model evaluation.