Preparazione al tuning

La preparazione al tuning è la base per un tuning di successo. Ci sono due passaggi principali: contrassegnare gli iperparametri usando tune() nella specifica del modello e creare una griglia di iperparametri da usare nel tuning.

In questo esercizio eseguirai questi due passaggi fondamentali del processo di tuning.

Questo esercizio fa parte del corso

Machine Learning con modelli ad albero in R

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Istruzioni dell'esercizio

Crea una specifica di boosting con motore "xgboost" per un modello di classificazione usando 500 alberi e contrassegna come parametri di tuning i seguenti parametri: learn_rate, tree_depth e sample_size. Salva il risultato come boost_spec.
Costruisci una griglia di tuning regolare per i parametri di tuning di boost_spec con tre livelli per ciascun parametro.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create the specification with placeholders
boost_spec <- boost_tree(
                trees = ___,
                ___,
                ___,
                ___) %>%
  set_mode(___) %>%
  set_engine(___)

# Create the tuning grid
tunegrid_boost <- ___(___, 
                      levels = ___)

tunegrid_boost

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Machine Learning con modelli ad albero in R

InicianteNível de habilidade

4.9+

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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?

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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: Introduzione al boosting Exercise 2: Bagging vs. boosting Exercise 3: Specifica un ensemble potenziato (boosted)Exercise 4: Gradient boosting Exercise 5: Allena un ensemble potenziato Exercise 6: Valuta l'insieme Exercise 7: Confronta con un singolo classificatore Exercise 8: Ottimizza l'ensemble potenziato Exercise 9: Preparazione al tuning

Esercizio in corso

Exercise 10: La vera fase di tuning Exercise 11: Finalizza il modello Exercise 12: Confronto tra modelli Exercise 13: Confronta l'AUC Exercise 14: Traccia le curve ROC Exercise 15: Riepilogo