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.

Continuous outcomes

Train a regression tree

Predict new values

Inspect model output

Performance metrics for regression trees

In-sample performance

Out-of-sample performance

Bigger mistakes, bigger penalty

Cross-validation

Create the folds

Fit the folds

Evaluate the folds

Bias-variance tradeoff

Call things by their names

Adjust model complexity

In-sample and out-of-sample performance

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.

Ottimizzazione degli iperparametri

Genera una griglia di tuning

Sintonizza lungo la griglia

Scegli il vincitore

Altre metriche del modello

Calcolare la specificità

Disegna la curva ROC

Area sotto la curva ROC

Alberi con bagging

Crea alberi bagged

ROC e AUC in-sample

Verifica l'overfitting

Random forest

Bagging vs. random forest

Importanza delle variabili

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

I modelli di Machine Learning basati su alberi possono mettere in luce relazioni non lineari complesse nei dati e spesso dominano le competizioni di machine learning. In questo corso userai il pacchetto tidymodels per esplorare e costruire diversi modelli ad albero — dai semplici alberi decisionali ai più complessi random forest. Imparerai anche a usare i boosted trees, una potente tecnica di Machine Learning che sfrutta l’ensemble learning per creare modelli predittivi ad alte prestazioni. Lungo il percorso, lavorerai con dati sanitari e di rischio di credito per prevedere l’incidenza del diabete e l’abbandono dei clienti.

Modeling with tidymodels in R

Impara a usare modelli ad albero e insiemi per previsioni di classificazione e regressione con tidymodels.

Machine Learning con modelli ad albero in R

Impara a usare modelli basati su alberi e insiemi per fare previsioni di classificazione e regressione con tidymodels.

Fondamenti di apprendimento automatico in R

Scienziato dell'apprendimento automatico in R

Apprendimento automatico supervisionato in R

Bagging vs. random forest

Machine Learning con modelli ad albero in R

Esercizio pratico interattivo