Optimaliteit van de support-confidencegrens

Je gaat terug naar de oprichter met de scatterplot uit de vorige oefening en vraagt of ze wil dat je pruning gebruikt om de support-confidencegrens te reconstrueren. Je vertelt haar over het Bayardo-Agrawal-resultaat, maar ze is sceptisch en vraagt of je dit met een voorbeeld kunt laten zien.

Omdat scatterplots de puntgrootte kunnen schalen op basis van een derde metric, besluit je dat te gebruiken om de optimaliteit van de support-confidencegrens te demonstreren. Je laat dit zien door de puntgrootte te schalen met de lift-metric, een van de metrics waarop Bayardo-Agrawal van toepassing is. De one-hot-gecodeerde gegevens zijn voor je geïmporteerd als onehot. Daarnaast zijn apriori() en association_rules() geïmporteerd en is pandas beschikbaar als pd.

Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

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Oefeninstructies

Pas het Apriori-algoritme toe op de DataFrame onehot.
Bereken de associatieregels met de metric support en een minimumdrempel van 0,0.
Maak de expressie voor de scatterplot af zodat de puntgrootte wordt geschaald door lift.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

SkillTag.level.intermediateSkillTag.label

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Begin de cursus 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: Heatmaps Exercise 2: Itemset-support visualiseren Exercise 3: Heatmaps met lift Exercise 4: Heatmaps interpreteren Exercise 5: Spreidingsdiagrammen Exercise 6: Prunen met scatterplots Exercise 7: Optimaliteit van de support-confidencegrens

Huidige oefening

Exercise 8: Parallel coordinates-plot Exercise 9: Regels visualiseren met parallelle coördinaten Exercise 10: Een parallelle-coördinatenplot verfijnen Exercise 11: Gefeliciteerd!