Pengujian out-of-sample

Tujuan Anda adalah membuat prediksi pembelian untuk toko-toko baru berdasarkan model yang telah Anda estimasi. Anda memeriksa apakah tujuan ini layak dilakukan dengan menggunakan fungsi predict(). Fungsi predict() mengambil estimasi parameter dari train.model untuk membuat prediksi terhadap variabel respons dalam test.data. Untuk memperoleh nilai prediksi pada skala variabel respons (probabilitas pembelian yang diprediksi), Anda harus mengatur argumen tambahan type ke "response".

Terakhir, prediksi hold-out diklasifikasikan menjadi pembelian dan bukan pembelian menggunakan fungsi ifelse() dan dibandingkan dengan pembelian yang teramati menggunakan fungsi table(). Terakhir, Anda menggunakan fungsi prop.table() untuk mengonversi angka dalam tabel menjadi angka relatif.

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

Petunjuk latihan

Prediksi respons untuk test.data dengan menggunakan fungsi predict() pada train.model. Atur argumen type ke "response" dan beri nama hasilnya probability.
Klasifikasikan prediksi model menjadi 1 jika probability melebihi 0.5 dan 0 jika tidak. Simpan hasilnya ke objek predicted.
Peroleh pembelian teramati untuk HOPPINESS dari test.data. Simpan ke objek observed.
Silangkan tabulasi vektor data observed dan predicted menggunakan fungsi table() dan prop.table().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Pemilihan model Exercise 2: Memperluas model respons logistik Exercise 3: Meringkas model Exercise 4: Prinsip deviance Exercise 5: Mengeliminasi prediktor Exercise 6: Kinerja prediktif Exercise 7: Klasifikasi Exercise 8: Kekeliruan model Exercise 9: Kurva ROC Exercise 10: Validasi model Exercise 11: Subsetting Exercise 12: Pelatihan model Exercise 13: Pengujian out-of-sample

Latihan Saat Ini

Exercise 14: Merangkum semuanya