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Machine Learning with Tree-Based Models in R

Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

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4 Horas16 Videos58 Ejercicios
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Descripción del curso

Tree-based machine learning models can reveal complex non-linear relationships in data and often dominate machine learning competitions. In this course, you'll use the tidymodels package to explore and build different tree-based models—from simple decision trees to complex random forests. You’ll also learn to use boosted trees, a powerful machine learning technique that uses ensemble learning to build high-performing predictive models. Along the way, you'll work with health and credit risk data to predict the incidence of diabetes and customer churn.
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  1. 1

    Classification Trees

    Gratuito

    Ready to build a real machine learning pipeline? Complete step-by-step exercises to learn how to create decision trees, split your data, and predict which patients are most likely to suffer from diabetes. Last but not least, you’ll build performance measures to assess your models and judge your predictions.

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    Welcome to the course!
    50 xp
    Why tree-based methods?
    100 xp
    Specify that tree
    100 xp
    Train that model
    100 xp
    How to grow your tree
    50 xp
    Train/test split
    100 xp
    Avoiding class imbalances
    100 xp
    From zero to hero
    100 xp
    Predict and evaluate
    50 xp
    Make predictions
    100 xp
    Crack the matrix
    100 xp
    Are you predicting correctly?
    100 xp
  2. 2

    Regression Trees and Cross-Validation

    Ready for some candy? Use a chocolate rating dataset to build regression trees and assess their performance using suitable error measures. You’ll overcome statistical insecurities of single train/test splits by applying sweet techniques like cross-validation and then dive even deeper by mastering the bias-variance tradeoff.

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  3. 3

    Hyperparameters and Ensemble Models

    Time to get serious with tuning your hyperparameters and interpreting receiver operating characteristic (ROC) curves. In this chapter, you’ll leverage the wisdom of the crowd with ensemble models like bagging or random forests and build ensembles that forecast which credit card customers are most likely to churn.

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  4. 4

    Boosted Trees

    Ready for the high society of tree-based models? Apply gradient boosting to create powerful ensembles that perform better than anything that you have seen or built. Learn about their fine-tuning and how to compare different models to pick a winner for production.

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En las siguientes pistas

Fundamentos del machine learning en R

Ir a la pista

Científico de Machine Learning con R

Ir a la pista

Aprendizaje automático supervisado en R

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Sets De Datos

Chocolate ratingsDiabetes riskBank customer churn

Colaboradores

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Maggie Matsui
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Justin Saddlemyer
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James Chapman
Sandro Raabe HeadshotSandro Raabe

Data Scientist

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