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Machine Learning in the Tidyverse

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更新日 2022/12

Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

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RMachine Learning5時間15 ビデオ52 演習4,300 XP16,227達成証明書

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コース説明

Welcome to the tidyverse! In this course, you will continue on your journey to learn the tidyverse and apply your knowledge to machine learning concepts.

This course is ideal if you’re looking to integrate R's Tidyverse tools into your machine learning workflows.

Evaluating machine learning models

Throughout this course, you will focus on leveraging the tidyverse tools in R to build, explore, and evaluate machine learning models efficiently.

The course begins by introducing the List Column Workflow (LCW), a method for managing multiple models within a single dataframe. It also covers using the broom package to tidy up and explore model outputs, making the complex results more interpretable.

Utilizing tidyr and purrr

Work through practical exercises including building and evaluating regression along with classification models. Explore techniques for tuning hyperparameters to optimize model performance.

You will use packages like tidyr and purrr to handle complex data manipulations and model evaluations, ensuring a tidy and systematic approach to machine learning.

Gain real-world application

Explore real-world examples through multiple case studies, such as using the gapminder dataset to predict life expectancy with linear models.

By the end of the course, you will have a strong foundation in applying Tidyverse principles to machine learning, enabling them to build, tune, and evaluate models efficiently in a tidy and reproducible manner.

前提条件

Modeling with Data in the Tidyverse

1

Foundations of "tidy" Machine learning

This chapter will introduce you to the backbone of machine learning in the tidyverse, the List Column Workflow (LCW). The LCW will empower you to work with many models in one dataframe.
This chapter will also introduce you to the fundamentals of the broom package for exploring your models.

Foundations of "tidy" machine learning

Nesting your data

Unnesting your data

Explore a nested cell

The map family of functions

Mapping your data

Expecting mapped output

Mapping many models

Tidy your models with broom

The three ways to tidy your model

Extracting model statistics tidily

Augmenting your data

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2

Multiple Models with broom

This chapter leverages the List Column Workflow to build and explore the attributes of 77 models. You will use the tools from the broom package to gain a multidimensional understanding of all of these models.

Exploring coefficients across models

Tidy up the coefficients of your models

What can we learn about these 77 countries?

Evaluating the fit of many models

Glance at the fit of your models

Best and worst fitting models

Visually inspect the fit of many models

Augment the fitted values of each model

Explore your best and worst fitting models

Improve the fit of your models

Build better models

Predicting the future

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3

Build, Tune & Evaluate Regression Models

In this chapter you will learn how to use the List Column Workflow to build, tune and evaluate regression models. You will have the chance to work with two types of models: linear models and random forest models.

Training, test and validation splits

The test-train split

Cross-validation data frames

Measuring cross-validation performance

Build cross-validated models

Preparing for evaluation

Evaluate model performance

Building and tuning a random forest model

Build a random forest model

Evaluate a random forest model

Fine tune your model

The best performing parameter

Measuring the test performance

Build & evaluate the best model

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4

Build, Tune & Evaluate Classification Models

In this chapter you will shift gears to build, tune and evaluate classification models.

Logistic regression models

Prepare train-test-validate parts

Build cross-validated models

Evaluating classification models

Predictions of a single model

Performance of a single model

Prepare for cross-validated performance

Calculate cross-validated performance

Random forest for classification

Tune random forest models

Random forest performance

Build final classification model

Measure final model performance

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Machine Learning in the Tidyverse

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