1. Learn
    2. /
  2. Courses
    3. /
  3. Modeling with tidymodels in R
  • 1

    Machine Learning with tidymodels

    Free

    In this chapter, you’ll explore the rich ecosystem of R packages that power tidymodels and learn how they can streamline your machine learning workflows. You’ll then put your tidymodels skills to the test by predicting house sale prices in Seattle, Washington.

  • 2

    Classification Models

    Learn how to predict categorical outcomes by training classification models. Using the skills you’ve gained so far, you’ll predict the likelihood of customers canceling their service with a telecommunications company.

  • 3

    Feature Engineering

    Find out how to bake feature engineering pipelines with the recipes package. You’ll prepare numeric and categorical data to help machine learning algorithms optimize your predictions.

  • 4

    Workflows and Hyperparameter Tuning

    Now it’s time to streamline the modeling process using workflows and fine-tune models with cross-validation and hyperparameter tuning. You’ll learn how to tune a decision tree classification model to predict whether a bank's customers are likely to default on their loan.

Initializing

Exercise

Random grid search

The most common method of hyperparameter tuning is grid search. This method creates a tuning grid with unique combinations of hyperparameter values and uses cross validation to evaluate their performance. The goal of hyperparameter tuning is to find the optimal combination of values for maximizing model performance.

In this exercise, you will create a random hyperparameter grid and tune your loans data decision tree model.

Your cross validation folds, loans_folds, workflow object, loans_tune_wkfl, custom metrics function, loans_metrics, and dt_tune_model have been loaded into your session.

Instructions 1/3

undefined XP
    1
    2
    3
  • Create a random grid of 5 hyperparameter value combinations using the hyperparameters of your dt_tune_model object.