# Tune better models

## Overview

• Define tree-based inference
• Identify hyperparameters for machine learning models
• Fit decision tree and random forest models
• Tune hyperparameters using a grid search
• Identify the best model and finalize the workflow

## Before class

This is not a math/stats class. In class we will briefly summarize how these methods work and spend the bulk of our time on estimating and interpreting these models. That said, you should have some understanding of the mathematical underpinnings of statistical learning methods prior to implementing them yourselves. See below for some recommended readings:

## Class materials

usethis::use_course("cis-ds/machine-learning")