Support mit Konfidenz verfeinern

Nachdem du deine Ergebnisse aus der vorherigen Übung vorgestellt hast, fragt dich die Bibliothek nach der Richtung der Beziehung. Soll Harry Potter genutzt werden, um Twilight zu bewerben, oder Twilight, um Harry Potter zu bewerben?

Nach einigem Überlegen entscheidest du dich, die Kennzahl Konfidenz zu berechnen, die im Gegensatz zum Support eine Richtung hat. Du berechnest sie für {Potter} \(\rightarrow\) {Twilight} und {Twilight} \(\rightarrow\) {Potter}. Der DataFrame books wurde bereits importiert und enthält je eine Spalte pro Buch: Potter und Twilight.

Diese Übung ist Teil des Kurses

<Kurs>Market Basket Analysis in Python</Kurs>

Übungsanweisungen

Berechne den Support von {Potter, Twilight}.
Berechne den Support von {Potter}.
Berechne den Support von {Twilight}.
Berechne die Konfidenz von {Potter} \(\rightarrow\) {Twilight} und {Twilight} \(\rightarrow\) {Potter}.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Compute support for Potter and Twilight
supportPT = np.logical_and(____, ____).mean()

# Compute support for Potter
supportP = books['Potter'].____

# Compute support for Twilight
supportT = ____

# Compute confidence for both rules
confidencePT = supportPT / ____
confidenceTP = ____ / supportT

# Print results
print('{0:.2f}, {1:.2f}'.format(confidencePT, confidenceTP))

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Market Basket Analysis in Python</Kurs>

Mittlere SchwierigkeitSchwierigkeitsgrad

4.9+

Kurs kostenlos starten

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: Konfidenz und Lift Exercise 2: Bücher mit Support empfehlen Exercise 3: Support mit Konfidenz verfeinern

Aktuelle Übung

Exercise 4: Weitere Verfeinerung mit Lift Exercise 5: Leverage und Conviction Exercise 6: Lift versus Leverage Exercise 7: Conviction berechnen Exercise 8: Conviction mit einer Funktion berechnen Exercise 9: E-Books mit Conviction bewerben Exercise 10: Assoziation und Dissoziation Exercise 11: Assoziation und Dissoziation berechnen Exercise 12: Zhangs Kennzahl definieren Exercise 13: Zhangs Metrik anwenden Exercise 14: Fortgeschrittene Regeln Exercise 15: Filtern mit Support und Conviction Exercise 16: Cross-Promotion von Büchern mit Multi-Metrik-Filterung

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!