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Machine Learning con caret in R
{hours} ore
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Se vuoi seguire il nostro corso Machine Learning Toolbox, ecco il link.
Calcolare una confusion matrix
Come hai visto nel video, una confusion matrix è uno strumento molto utile per calibrare l’output di un modello ed esaminare tutte le possibili conseguenze delle tue previsioni (vero positivo, vero negativo, falso positivo, falso negativo).
Prima di creare la tua confusion matrix, devi "tagliare" le probabilità previste a una certa soglia per trasformarle in predizioni di classe. Puoi farlo facilmente con la funzione ifelse(), ad esempio:
class_prediction <-
ifelse(probability_prediction > 0.50,
"positive_class",
"negative_class"
)
Potresti creare una tabella di contingenza con la funzione table() di base R, ma confusionMatrix() in caret restituisce molte utili statistiche accessorie oltre ai tassi di base della tabella. Puoi calcolare la confusion matrix (e le statistiche associate) usando gli esiti previsti e quelli reali, ad esempio:
confusionMatrix(predicted, actual)
Istruzioni
- Trasforma le previsioni numeriche
pin un vettore di predizioni di classe chiamatop_class, usando un cutoff di previsione di 0,50. Assicurati di usare"M"per la classe positiva e"R"per la classe negativa quando fai le previsioni, per corrispondere alle classi nei dati originali. - Crea una confusion matrix usando
p_class, i valori reali nel setteste la funzioneconfusionMatrix().
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Se tutto ti è chiaro, prosegui con l’esercizio successivo! In caso contrario, ecco un video panoramico.
Video panoramico sulla Confusion Matrix
Dalle probabilità alla confusion matrix
Viceversa, poniamo che tu voglia essere davvero certo che il tuo modello identifichi correttamente tutte le mine come mine. In questo caso, potresti usare una soglia di previsione di 0,10 invece di 0,90.
Puoi costruire la confusion matrix nello stesso modo di prima, usando le nuove classi previste:
pred <- ifelse(probability > threshold, "M", "R")
Puoi poi chiamare la funzione confusionMatrix() nello stesso modo dell’esercizio precedente:
confusionMatrix(pred, actual)
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Argomenti
