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Machine Learning in the Tidyverse

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

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5 Horas15 Videos52 Ejercicios

14.356 AprendicesDeclaración de cumplimiento

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Descripción del curso

This course will teach you to leverage the tools in the "tidyverse" to generate, explore, and evaluate machine learning models. Using a combination of tidyr and purrr packages, you will build a foundation for how to work with complex model objects in a "tidy" way. You will also learn how to leverage the broom package to explore your resulting models. You will then be introduced to the tools in the test-train-validate workflow, which will empower you evaluate the performance of both classification and regression models as well as provide the necessary information to optimize model performance via hyperparameter tuning.

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En las siguientes pistas

Caja de herramientas intermedia de Tidyverse

Científico de Machine Learning con R

Aprendizaje automático supervisado en R

1
Foundations of "tidy" Machine learning
Gratuito
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.
Reproducir Capítulo Ahora
Foundations of "tidy" machine learning
50 xp
Nesting your data
100 xp
Unnesting your data
100 xp
Explore a nested cell
100 xp
The map family of functions
50 xp
Mapping your data
100 xp
Expecting mapped output
100 xp
Mapping many models
100 xp
Tidy your models with broom
50 xp
The three ways to tidy your model
50 xp
Extracting model statistics tidily
100 xp
Augmenting your data
100 xp
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.
Reproducir Capítulo Ahora
Exploring coefficients across models
50 xp
Tidy up the coefficients of your models
100 xp
What can we learn about these 77 countries?
50 xp
Evaluating the fit of many models
50 xp
Glance at the fit of your models
100 xp
Best and worst fitting models
100 xp
Visually inspect the fit of many models
50 xp
Augment the fitted values of each model
100 xp
Explore your best and worst fitting models
100 xp
Improve the fit of your models
50 xp
Build better models
100 xp
Predicting the future
50 xp
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.
Reproducir Capítulo Ahora
Training, test and validation splits
50 xp
The test-train split
100 xp
Cross-validation data frames
100 xp
Measuring cross-validation performance
50 xp
Build cross-validated models
100 xp
Preparing for evaluation
100 xp
Evaluate model performance
100 xp
Building and tuning a random forest model
50 xp
Build a random forest model
100 xp
Evaluate a random forest model
100 xp
Fine tune your model
100 xp
The best performing parameter
100 xp
Measuring the test performance
50 xp
Build & evaluate the best model
100 xp
4
Build, Tune & Evaluate Classification Models
In this chapter you will shift gears to build, tune and evaluate classification models.
Reproducir Capítulo Ahora
Logistic regression models
50 xp
Prepare train-test-validate parts
100 xp
Build cross-validated models
100 xp
Evaluating classification models
50 xp
Predictions of a single model
100 xp
Performance of a single model
100 xp
Prepare for cross-validated performance
100 xp
Calculate cross-validated performance
100 xp
Random forest for classification
50 xp
Tune random forest models
100 xp
Random forest performance
100 xp
Build final classification model
100 xp
Measure final model performance
100 xp
Wrap-up
50 xp

Empresas

¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

En las siguientes pistas

Caja de herramientas intermedia de Tidyverse

Científico de Machine Learning con R

Aprendizaje automático supervisado en R

Sets De Datos

Gapminder Attrition

Colaboradores

Chester Ismay

Sumedh Panchadhar

Eunkyung Park

Requisitos Previos

Modeling with Data in the Tidyverse

Dmitriy Gorenshteyn

Lead Data Scientist at Memorial Sloan Kettering Cancer Center

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