Confusione del modello

Su richiesta, hai costruito un classificatore ingenuo. Il management del birrificio vuole lavorare su queste cifre per adattare il piano di marketing. Quindi, la tua classificazione è davvero corretta?

Incrociando (cross-tabulation) gli acquisti osservati vs. quelli classificati puoi verificare le prestazioni del tuo classificatore. La cross-tabulation si può fare usando la funzione table() sugli eventi di acquisto predicted insieme ai veri acquisti observed. Per ottenere valori relativi, dividi inoltre la tabella per il numero totale di osservazioni. In alternativa, puoi usare la funzione prop.table() per convertire i conteggi della tabella in valori relativi.

Questo esercizio fa parte del corso

Creare modelli di risposta in R

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Istruzioni dell'esercizio

Ottieni gli acquisti osservati per HOPPINESS da choice.data e assegnali a un oggetto observed.
Fai la cross-tabulation dei vettori observed e predicted usando la funzione table(). Dividi il risultato per 2798.
Fai la cross-tabulation dei vettori observed e predicted usando la funzione table(). Applica la funzione prop.table() al risultato.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Obtain the observed purchases
observed <- ___

# Cross-tabulate observed vs. predicted purchases
___(predicted, observed)/___

# Cross-tabulate observed vs. predicted purchases
___(___(predicted, observed))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Creare modelli di risposta in R

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The first chapter introduces you to the basic principles and concepts of market response models. Here, you will learn how to build simple response models for product sales. In addition, you will learn about the theoretical and practical differences between linear and non-linear models for sales responses.

Exercise 1: Fundamentals of market response models Exercise 2: Retail sales Exercise 3: Understanding sales Exercise 4: Linear response models Exercise 5: A linear response model for sales Exercise 6: Making predictions Exercise 7: Predictive performance Exercise 8: Nonlinear response models Exercise 9: Linearizing nonlinear functions Exercise 10: What 's the value added?

An effective marketing strategy combines all the tools available to communicate the benefits of a product. The key is crafting the right mix of these tools to achieve sales increases and market share goals. In the second chapter, you will learn how to incorporate the effects of advertising and promotion in your sales-response model and how to identify the marketing strategy that is most likely to succeed.

Exercise 1: Model extension part 1: Dummy variables Exercise 2: Understanding dummy variables Exercise 3: The effect of display on sales Exercise 4: The effect of multiple dummies on sales Exercise 5: What about price?Exercise 6: Model extensions part 2: Dynamic variables Exercise 7: How to lag?Exercise 8: Adding lagged price effects Exercise 9: More lags Exercise 10: What's the value added?Exercise 11: How many extensions are needed?Exercise 12: Summarizing the model Exercise 13: Unnecessary predictors Exercise 14: Dropping predictors Exercise 15: Eliminating predictors

A company can only be successful in the market if its products have a competitive advantage over those of its rivals. To develop an effective marketing strategy in a competitive environment, it is essential to understand the interrelationship between marketing activity and customer behavior. In this chapter, you will learn how to explain the effects of temporary price changes on customer brand choice by employing logistic and probit response models.

Exercise 1: Models for individual demand Exercise 2: Customer purchases Exercise 3: Summarizing the data Exercise 4: Competition Exercise 5: A linear probability model for beer demand Exercise 6: Logistic response models Exercise 7: A logistic model for beer demand Exercise 8: Bounded predictions Exercise 9: Average marginal effects Exercise 10: Effect plots Exercise 11: Probit response models Exercise 12: A probit model for beer demand Exercise 13: Logistic vs. probit Exercise 14: Model comparison

The main goal of response modeling is to enable marketers to not only see a payoff for their actions today, but also tomorrow. In order to view this future payoff, a simple but reliable statistical model is required. In this last chapter, you will learn how to evaluate the predictive performance of logistic response models.

Exercise 1: Selezione del modello Exercise 2: Ampliare il modello di risposta logistica Exercise 3: Riepilogare il modello Exercise 4: Il principio della devianza Exercise 5: Eliminare i predittori Exercise 6: Prestazioni predittive Exercise 7: Classificazioni Exercise 8: Confusione del modello

Esercizio in corso

Exercise 9: Curve ROC Exercise 10: Validazione del modello Exercise 11: Sottoinsiemi Exercise 12: Addestramento del modello Exercise 13: Test fuori campione Exercise 14: Tiriamo le somme