Confidence dan lift

Analisis Market Basket dengan Python

Isaiah Hull

Visiting Associate Professor of Finance, BI Norwegian Business School

Saat support menyesatkan

TID	Transaction
1	Coffee, Milk
2	Bread, Milk, Orange
3	Bread, Milk
4	Bread, Milk, Sugar
5	Bread, Jam, Milk
...	...

Milk dan bread sering dibeli bersama.
- Support: {Milk} $\rightarrow$ {Bread}
Aturan kurang informatif untuk pemasaran.
- Milk dan bread adalah item populer.

Metrik confidence

Dapat meningkatkan di atas support dengan metrik tambahan.
Menambahkan confidence memberi gambaran lebih lengkap.

$$\frac{Support(X \& Y)}{Support(X)}$$

Menafsirkan metrik confidence

Gambar ini menampilkan daftar transaksi di toko kelontong. Salah satunya memuat selai dan roti.

$$Support(Milk\&Coffee) = 0.20$$

Gambar ini menampilkan daftar transaksi di toko kelontong. Salah satunya memuat selai dan roti.

$$Support(Milk) = 1.00$$

Menafsirkan metrik confidence

$$\frac{Support(Milk\&Coffee)}{Support(Milk)} = \frac{0.20}{1.00} = 0.20$$

$$\frac{Support(Milk\&Coffee)}{Support(Milk)} = \frac{0.20}{0.80} = 0.25$$

$$\frac{Support(Milk\&Coffee)}{Support(Milk)} = \frac{0.20}{0.20} = 1.00$$

Metrik lift

Lift memberi metrik lain untuk menilai hubungan antarelemen.
- Pembilang: Proporsi transaksi yang memuat X dan Y.
- Penyebut: Proporsi jika X dan Y muncul acak dan independen.

$$\frac{Support(X \& Y)}{Support(X) Support(Y)}$$

Menyiapkan data

from mlxtend.preprocessing import TransactionEncoder
import pandas as pd

# Split library strings into lists
libraries = data['Library'].apply(lambda t: t.split(','))

# Convert to list of lists
libraries = list(libraries)

# One-hot encode books
books = TransactionEncoder().fit(libraries).transform(libraries)

# Convert one-hot encoded data to DataFrame
books = pd.DataFrame(books, columns = encoder.columns_)

Menghitung confidence dan lift

# Print first five items
print(books.head())

       Hunger           Gatsby
0      False            True
1      False            True
2      False            False
3      False            True
4      False            True

Dataset: GoodBooks-10K.

Menghitung confidence dan lift

# Computing support.
supportHG = np.logical_and(books['Hunger'],books['Gatsby']).mean()
supportH = books['Hunger'].mean()
supportG = books['Gatsby'].mean()

# Compute and print confidence and lift.
confidence = supportHG / supportH
lift = supportHG / (supportH * supportG)

# Print results.
print(supportG, confidence, lift)

(0.30, 0.16, 0.53)

Ayo berlatih!

Analisis Market Basket dengan Python