Esta sección ha sido probado por expertos así garantizamos la veracidad de nuestro contenido.
Ejemplo: visualización de la regla de asociación con Python
dataset =[['Milk','Onion','Nutmeg','Kidney Beans','Eggs','Yogurt'],['Dill','Onion','Nutmeg','Kidney Beans','Eggs','Yogurt'],['Milk','Apple','Kidney Beans','Eggs'],['Milk','Unicorn','Corn','Kidney Beans','Yogurt'],['Corn','Onion','Onion','Kidney Beans','Ice cream','Eggs']]import pandas as pd
from mlxtend.preprocessing import OnehotTransactions
from mlxtend.frequent_patterns import apriori
oht = OnehotTransactions()
oht_ary = oht.fit(dataset).transform(dataset)
df = pd.DataFrame(oht_ary, columns=oht.columns_)print(df)
frequent_itemsets = apriori(df, min_support=0.6, use_colnames=True)print(frequent_itemsets)
association_rules(frequent_itemsets, metric="confidence", min_threshold=0.7)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.2)print(rules)"""
Below is the output
support itemsets
0 0.8 [Eggs]
1 1.0 [Kidney Beans]
2 0.6 [Milk]
3 0.6 [Onion]
4 0.6 [Yogurt]
5 0.8 [Eggs, Kidney Beans]
6 0.6 [Eggs, Onion]
7 0.6 [Kidney Beans, Milk]
8 0.6 [Kidney Beans, Onion]
9 0.6 [Kidney Beans, Yogurt]
10 0.6 [Eggs, Kidney Beans, Onion]
antecedants consequents support confidence lift
0 (Kidney Beans, Onion) (Eggs) 0.6 1.00 1.25
1 (Kidney Beans, Eggs) (Onion) 0.8 0.75 1.25
2 (Onion) (Kidney Beans, Eggs) 0.6 1.00 1.25
3 (Eggs) (Kidney Beans, Onion) 0.8 0.75 1.25
4 (Onion) (Eggs) 0.6 1.00 1.25
5 (Eggs) (Onion) 0.8 0.75 1.25
"""Si posees alguna desconfianza y disposición de enriquecer nuestro tutorial eres capaz de añadir una crítica y con gusto lo analizaremos.
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