Optimalitas batas support-confidence

Anda kembali kepada pendiri dengan scatterplot yang dihasilkan pada latihan sebelumnya dan menanyakan apakah ia ingin Anda menggunakan pruning untuk memperoleh kembali batas support-confidence. Anda menceritakan tentang hasil Bayardo-Agrawal, tetapi ia tampak skeptis dan meminta apakah Anda dapat mendemonstrasikannya dengan sebuah contoh.

Mengingat bahwa scatterplot dapat menskalakan ukuran titik berdasarkan metrik ketiga, Anda memutuskan menggunakannya untuk menunjukkan optimalitas batas support-confidence. Anda akan menunjukkan ini dengan menskalakan ukuran titik menggunakan metrik lift, yang merupakan salah satu metrik yang dibahas oleh Bayardo-Agrawal. Data one-hot encoded telah diimpor untuk Anda dan tersedia sebagai onehot. Selain itu, apriori() dan association_rules() telah diimpor dan pandas tersedia sebagai pd.

Latihan ini adalah bagian dari kursus

Analisis Market Basket dengan Python

Petunjuk latihan

Terapkan algoritma Apriori pada DataFrame onehot.
Hitung aturan asosiasi menggunakan metrik support dan ambang minimum 0.0.
Lengkapi ekspresi untuk scatterplot sehingga ukuran titik diskalakan oleh lift.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import seaborn under its standard alias
import seaborn as sns

# Apply the Apriori algorithm with a support value of 0.0075
frequent_itemsets = ____(____, min_support = 0.0075, 
                         use_colnames = True, max_len = 2)

# Generate association rules without performing additional pruning
rules = ____(frequent_itemsets, metric = "support", 
                          min_threshold = ____)

# Generate scatterplot using support and confidence
sns.scatterplot(x = "support", y = "confidence", 
                size = "____", data = rules)
plt.show()

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 and lift Exercise 2: Recommending books with support Exercise 3: Refining support with confidence Exercise 4: Further refinement with lift Exercise 5: Leverage and conviction Exercise 6: Lift versus leverage Exercise 7: Computing conviction Exercise 8: Computing conviction with a function Exercise 9: Promoting ebooks with conviction Exercise 10: Association and dissociation Exercise 11: Computing association and dissociation Exercise 12: Defining Zhang's metric Exercise 13: Applying Zhang's metric Exercise 14: Advanced rules Exercise 15: Filtering with support and conviction Exercise 16: Using multi-metric filtering to cross-promote books

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: Heatmap Exercise 2: Memvisualisasikan dukungan itemset Exercise 3: Heatmap dengan lift Exercise 4: Menafsirkan heatmap Exercise 5: Scatterplot Exercise 6: Pemangkasan dengan scatterplot Exercise 7: Optimalitas batas support-confidence

Latihan Saat Ini

Exercise 8: Plot koordinat paralel Exercise 9: Menggunakan koordinat paralel untuk memvisualisasikan aturan Exercise 10: Menyempurnakan plot koordinat paralel Exercise 11: Selamat!