Predici nuovi valori

Un modello predittivo è un modello che prevede gli esiti di nuovi dati mai visti. Oltre ai predittori numerici, nel set di dati ci sono altre colonne utili. L'obiettivo di questo esercizio è prevedere i voti finali di una degustazione di cioccolato sulla base di tutte le altre variabili predittive disponibili.

Nel tuo workspace sono già caricati la specifica dell'albero di regressione creata nell'esercizio precedente, chocolate_spec, e i dati di training e test (chocolate_train e chocolate_test).

Questo esercizio fa parte del corso

Machine Learning con modelli ad albero in R

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Istruzioni dell'esercizio

Usa model_spec per addestrare un albero di regressione chocolate_model che predica final_grade usando tutti i predittori nei dati.
Predici i valori di final_grade usando la tibble chocolate_test.
Aggiungi i risultati alla tibble chocolate_test.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Train the model
chocolate_model <- model_spec %>%
  ___

# Predict new data
predictions <- predict(___,
                       ___) %>%
  # Add the test set
  bind_cols(___)

predictions

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Machine Learning con modelli ad albero in R

InicianteNível de habilidade

4.9+

Inizia il corso gratis

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.

Exercise 1: Welcome to the course!Exercise 2: Why tree-based methods?Exercise 3: Specify that tree Exercise 4: Train that model Exercise 5: How to grow your tree Exercise 6: Train/test split Exercise 7: Avoiding class imbalances Exercise 8: From zero to hero Exercise 9: Predict and evaluate Exercise 10: Make predictions Exercise 11: Crack the matrix Exercise 12: Are you predicting correctly?

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.

Exercise 1: Esiti continui Exercise 2: Allena un albero di regressione Exercise 3: Predici nuovi valori

Esercizio in corso

Exercise 4: Esamina l'output del modello Exercise 5: Metriche di performance per gli alberi di regressione Exercise 6: Prestazioni in-sample Exercise 7: Prestazioni out-of-sample Exercise 8: Errori più grandi, penalità più grande Exercise 9: Cross-validation Exercise 10: Crea i fold Exercise 11: Esegui il fitting delle fold Exercise 12: Valuta le fold Exercise 13: Compromesso bias-varianza Exercise 14: Chiama le cose col loro nome Exercise 15: Regola la complessità del modello Exercise 16: Prestazioni in-sample e out-of-sample

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.

Exercise 1: Tuning hyperparameters Exercise 2: Generate a tuning grid Exercise 3: Tune along the grid Exercise 4: Pick the winner Exercise 5: More model measures Exercise 6: Calculate specificity Exercise 7: Draw the ROC curve Exercise 8: Area under the ROC curve Exercise 9: Bagged trees Exercise 10: Create bagged trees Exercise 11: In-sample ROC and AUC Exercise 12: Check for overfitting Exercise 13: Random forest Exercise 14: Bagged trees vs. random forest Exercise 15: Variable importance

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.

Exercise 1: Introduction to boosting Exercise 2: Bagging vs. boosting Exercise 3: Specify a boosted ensemble Exercise 4: Gradient boosting Exercise 5: Train a boosted ensemble Exercise 6: Evaluate the ensemble Exercise 7: Compare to a single classifier Exercise 8: Optimize the boosted ensemble Exercise 9: Tuning preparation Exercise 10: The actual tuning Exercise 11: Finalize the model Exercise 12: Model comparison Exercise 13: Compare AUC Exercise 14: Plot ROC curves Exercise 15: Wrap-up