Geavanceerde regels
Market Basket Analysis in Python
Isaiah Hull
Visiting Associate Professor of Finance, BI Norwegian Business School
Overzicht van market-basketanalyse
- Standaardprocedure voor market-basketanalyse.
- Genereer veel regels.
- Filter met metriek.
- Gebruik intuïtie en gezond verstand.
Regels genereren
- Aantal regels groeit exponentieel met aantal items.
- De meeste regels zijn niet nuttig.
- Eerste filterronde is nodig.
- In hoofdstuk 3 met Apriori-algoritme
Hoe werkt filteren?
| ID | antecedents | consequents | support | Lift |
|---|---|---|---|---|
| 1 | Harry Potter | The Hunger Games | 0.001 | 1.01 |
| 2 | Hunger Games | Twilight | 0.020 | 1.23 |
| 3 | Pride and Prejudice | The Hobbit | 0.030 | 1.05 |
| 4 | The Hobbit | Twilight | 0.015 | 0.85 |
| 5 | Harry Potter | The Hobbit | 0.010 | 1.07 |
Multi-metrische filtering
| ID | antecedents | consequents | support | zhang |
|---|---|---|---|---|
| 1 | Harry Potter | The Hunger Games | 0.001 | -0.05 |
| 2 | Hunger Games | Twilight | 0.020 | 0.17 |
| 3 | Pride and Prejudice | The Hobbit | 0.030 | 0.06 |
| 4 | The Hobbit | Twilight | 0.015 | -0.04 |
| 5 | Harry Potter | The Hobbit | 0.010 | 0.34 |
Multi-metrische filtering toepassen
print(rules.head())
antecedents consequents antecedent support ... support confidence ... conviction
0 Potter Hunger 0.48 ... 0.12 ... 0.26 ... 0.92
1 Potter Hunger 0.32 ... 0.12 ... 0.26 ... 0.92
2 Twilight Hunger 0.26 ... 0.09 ... 0.35 ... 1.04
3 Hunger Twilight 0.32 ... 0.09 ... 0.28 ... 1.03
4 Mockingbird Hunger 0.48 ... 0.10 ... 0.20 ... 0.85
Multi-metrische filtering toepassen
# Selecteer subset van regels met lage consequent-support.
rules = rules[rules['consequent support'] < 0.05]
print(len(rules))
12
# Selecteer subset van regels met lift > 1.5.
rules = rules[rules['lift'] > 1.5]
print(len(rules))
2
Laten we oefenen!
Market Basket Analysis in Python
