Menghitung conviction dengan sebuah fungsi

Setelah berhasil menyelesaikan proyek percobaan, pendiri startup ebook tersebut memutuskan untuk merekrut Anda untuk proyek yang jauh lebih besar. Ia menanyakan apakah Anda dapat menghitung conviction untuk setiap pasangan buku dalam himpunan data goodreads-10k, sehingga ia dapat menggunakan informasi tersebut untuk menentukan buku mana yang perlu ditempatkan lebih berdekatan di situs web.

Anda setuju mengambil pekerjaan itu, tetapi menyadari bahwa Anda memerlukan cara yang lebih efisien untuk menghitung conviction, karena Anda akan menghitungnya berkali-kali. Anda memutuskan menulis sebuah fungsi yang menghitungnya. Fungsi ini akan menerima dua kolom dari DataFrame pandas sebagai masukan, satu sebagai antecedent dan satu sebagai consequent, lalu menghasilkan metrik conviction. Perhatikan bahwa pandas tersedia sebagai pd dan numpy tersedia sebagai np.

Latihan ini adalah bagian dari kursus

Analisis Market Basket dengan Python

Petunjuk latihan

Hitung support untuk antecedent dan simpan ke supportA.
Hitung support untuk BUKAN consequent.
Hitung support untuk antecedent dan BUKAN consequent.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

def conviction(antecedent, consequent):
	# Compute support for antecedent AND consequent
	supportAC = np.logical_and(antecedent, consequent).mean()

	# Compute support for antecedent
	supportA = ____.____()

	# Compute support for NOT consequent
	supportnC = 1.0 - ____.____()

	# Compute support for antecedent and NOT consequent
	supportAnC = ____ - supportAC

    # Return conviction
	return supportA * supportnC / supportAnC

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Analisis Market Basket dengan Python

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

In this chapter, you’ll learn the basics of Market Basket Analysis: association rules, metrics, and pruning. You’ll then apply these concepts to help a small grocery store improve its promotional and product placement efforts.

Exercise 1: What is market basket analysis?Exercise 2: The basics of market basket analysis Exercise 3: Cross-selling products Exercise 4: Identifying association rules Exercise 5: Multiple antecedents and consequents Exercise 6: Preparing data for market basket analysis Exercise 7: Generating association rules Exercise 8: The simplest metric Exercise 9: One-hot encoding transaction data Exercise 10: Computing the support metric

Association rules tell us that two or more items are related. Metrics allow us to quantify the usefulness of those relationships. In this chapter, you’ll apply six metrics to evaluate association rules: supply, confidence, lift, conviction, leverage, and Zhang's metric. You’ll then use association rules and metrics to assist a library and an e-book seller.

Exercise 1: Confidence dan lift Exercise 2: Merekomendasikan buku dengan support Exercise 3: Menyempurnakan support dengan confidence Exercise 4: Penyempurnaan lebih lanjut dengan lift Exercise 5: Leverage dan conviction Exercise 6: Lift versus leverage Exercise 7: Menghitung conviction Exercise 8: Menghitung conviction dengan sebuah fungsi

Latihan Saat Ini

Exercise 9: Mempromosikan e-book dengan conviction Exercise 10: Asosiasi dan disosiasi Exercise 11: Menghitung asosiasi dan disosiasi Exercise 12: Mendefinisikan metrik Zhang Exercise 13: Menerapkan metrik Zhang Exercise 14: Aturan lanjutan Exercise 15: Penyaringan dengan support dan conviction Exercise 16: Menggunakan penyaringan multi-metrik untuk promosi silang buku

The fundamental problem of Market Basket Analysis is determining how to translate vast amounts of customer decisions into a small number of useful rules. This process typically starts with the application of the Apriori algorithm and involves the use of additional strategies, such as pruning and aggregation. In this chapter, you’ll learn how to use these methods and will ultimately apply them in exercises where you assist a retailer in selecting a physical store layout and performing product cross-promotions.

Exercise 1: Aggregation Exercise 2: Performing aggregation Exercise 3: Defining an aggregation function Exercise 4: The Apriori algorithm Exercise 5: Pruning and Apriori Exercise 6: Identifying frequent itemsets with Apriori Exercise 7: Selecting a support threshold Exercise 8: Basic Apriori results pruning Exercise 9: Generating association rules Exercise 10: Pruning with lift Exercise 11: Pruning with confidence Exercise 12: Advanced Apriori results pruning Exercise 13: Aggregation and filtering Exercise 14: Applying Zhang's rule Exercise 15: Advanced filtering with multiple metrics

In this final chapter, you’ll learn how visualizations are used to guide the pruning process and summarize final results, which will typically take the form of itemsets or rules. You’ll master the three most useful visualizations -- heatmaps, scatterplots, and parallel coordinates plots – and will apply them to assist a movie streaming service.

Exercise 1: Heatmaps Exercise 2: Visualizing itemset support Exercise 3: Heatmaps with lift Exercise 4: Interpreting heatmaps Exercise 5: Scatterplots Exercise 6: Pruning with scatterplots Exercise 7: Optimality of the support-confidence border Exercise 8: Parallel coordinates plot Exercise 9: Using parallel coordinates to visualize rules Exercise 10: Refining a parallel coordinates plot Exercise 11: Congratulations!