Kekeliruan model

Sesuai permintaan, Anda membangun sebuah pengklasifikasi naif. Manajemen pabrik bir ingin menggunakan angka-angka ini untuk menyesuaikan rencana pemasaran mereka. Jadi, apakah klasifikasi Anda benar-benar tepat?

Dengan membuat tabulasi silang antara kejadian pembelian teramati vs. terklasifikasi, Anda dapat memeriksa kinerja pengklasifikasi Anda. Tabulasi silang dapat dilakukan dengan menggunakan fungsi table() pada kejadian pembelian predicted bersama dengan pembelian observed yang sebenarnya. Untuk memperoleh angka relatif, Anda juga membagi tabel dengan jumlah total observasi. Sebagai alternatif, Anda dapat 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

Peroleh pembelian yang teramati untuk HOPPINESS dari choice.data dan tetapkan ke objek observed.
Buat tabulasi silang vektor data observed dan predicted dengan menggunakan fungsi table(). Bagi hasilnya dengan 2798.
Buat tabulasi silang vektor data observed dan predicted dengan menggunakan fungsi table(). Terapkan fungsi prop.table() pada hasilnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Obtain the observed purchases
observed <- ___

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

# 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

Latihan Saat Ini

Exercise 9: Kurva ROC Exercise 10: Validasi model Exercise 11: Subsetting Exercise 12: Pelatihan model Exercise 13: Pengujian out-of-sample Exercise 14: Merangkum semuanya