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.

Welcome to the course!

Why tree-based methods?

Specify that tree

Train that model

How to grow your tree

Train/test split

Avoiding class imbalances

From zero to hero

Predict and evaluate

Make predictions

Crack the matrix

Are you predicting correctly?

Classification Trees

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.

Continue uitkomsten

Train een regressieboom

Nieuwe waarden voorspellen

Inspecteer de modeloutput

Prestatiemetrieken voor regressiebomen

Prestatie binnen de trainingsset

Out-of-sample-prestatie

Grotere fouten, zwaardere straf

Cross-validatie

Maak de folds

De folds fitten

Evalueer de folds

Afweging tussen bias en variantie

Begrippenbak

Overfitting

Underfitting

Noem dingen bij hun naam

Modelcomplexiteit aanpassen

Prestaties in-sample en out-of-sample

Regression Trees and Cross-Validation

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.

Tuning hyperparameters

Generate a tuning grid

Tune along the grid

Pick the winner

More model measures

Calculate specificity

Draw the ROC curve

Area under the ROC curve

Bagged trees

Create bagged trees

In-sample ROC and AUC

Check for overfitting

Random forest

Bagged trees vs. random forest

Variable importance

Hyperparameters and Ensemble Models

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.

Introduction to boosting

Bagging vs. boosting

Specify a boosted ensemble

Gradient boosting

Train a boosted ensemble

Evaluate the ensemble

Compare to a single classifier

Optimize the boosted ensemble

Tuning preparation

The actual tuning

Finalize the model

Model comparison

Compare AUC

Plot ROC curves

Wrap-up

Boosted Trees

Chocolate ratings

Diabetes risk

Bank customer churn

Boomgebaseerde Machine Learning-modellen kunnen complexe niet-lineaire relaties in data zichtbaar maken en domineren vaak Machine Learning-wedstrijden. In deze cursus gebruik je het tidymodels-pakket om verschillende boomgebaseerde modellen te verkennen en te bouwen—van eenvoudige beslissingsbomen tot complexe random forests. Je leert ook boosted trees gebruiken, een krachtige Machine Learning-techniek die ensemble learning inzet om sterk presterende voorspellende modellen te bouwen. Onderweg werk je met gezondheids- en kredietrisicodata om het optreden van diabetes en klantverloop te voorspellen.

Modeling with tidymodels in R

Leer hoe je boommodellen en ensembles gebruikt voor classificatie en regressie met tidymodels.

Machine Learning met boomgebaseerde modellen in R

Leer hoe je boomgebaseerde modellen en ensembles kunt gebruiken om classificatie- en regressievoorspellingen te doen met tidymodels.

Prestatiemetrieken voor regressiebomen

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