Örnek dışı test

Amacın, tahmin ettiğin modele dayanarak yeni mağazalar için satın alma tahminleri yapmak. Bu hedefin uygulanabilir olup olmadığını predict() işlevini kullanarak kontrol ediyorsun. predict() işlevi, train.model içindeki parametre tahminlerini alıp test.data içindeki tepki değişkeni hakkında tahminde bulunur. Tepki değişkeninin ölçeğinde (tahmin edilen satın alma olasılıkları) değerler elde etmek için ek type argümanını "response" olarak ayarlaman gerekir.

Son olarak, ayırılmış (hold-out) tahminler ifelse() işleviyle satın alma ve satın almama olarak sınıflandırılır ve table() işleviyle gözlenen satın almalarla karşılaştırılır. En sonunda, tablodaki sayıların oransal değerlere çevrilmesi için prop.table() işlevini kullanırsın.

Bu egzersiz

R ile Tepki Modelleri Oluşturma

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

train.model üzerinde predict() işlevini kullanarak test.data için tepkileri tahmin et. type argümanını "response" olarak ayarla ve sonucu probability olarak adlandır.
Model tahminlerini, probability 0.5'i aşıyorsa 1, aksi halde 0 olacak şekilde sınıflandır. Sonucu predicted adlı bir nesneye ata.
test.data içinden HOPPINESS için gözlenen satın almaları elde et. Bunları observed adlı bir nesneye ata.
observed ve predicted veri vektörlerini table() ve prop.table() işlevlerini kullanarak çapraz tabloya dönüştür.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Predict the purchase probabilities for test.data
probability <- ___(___, ___, type = "response") 

# Classify the predictions
predicted <- ___(probability >= ___, ___, ___) 

# Obtain the observed purchases from test.data
observed <- test.data$HOPPINESS

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

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Tepki Modelleri Oluşturma

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Model seçimi Exercise 2: Lojistik yanıt modelini genişletme Exercise 3: Modeli özetleme Exercise 4: Sapma ilkesi Exercise 5: Yordayıcıları elemek Exercise 6: Öngörü performansı Exercise 7: Sınıflandırmalar Exercise 8: Model karmaşası Exercise 9: ROC eğrileri Exercise 10: Model doğrulama Exercise 11: Alt Kümeleme Exercise 12: Model eğitimi Exercise 13: Örnek dışı test

Geçerli Egzersiz

Exercise 14: Kapanış