Skalakan dan transformasi data baru

Rekayasa Fitur untuk Machine Learning di Python

Robet O'Callaghan

Director of Data Science, Ordergroove

Gunakan ulang scaler pelatihan

scaler = StandardScaler()

scaler.fit(train[['col']])

train['scaled_col'] = scaler.transform(train[['col']])

# FIT SOME MODEL
# ....

test = pd.read_csv('test_csv')

test['scaled_col'] = scaler.transform(test[['col']])

Transformasi pelatihan untuk digunakan ulang

train_mean = train[['col']].mean()
train_std = train[['col']].std()

cut_off = train_std * 3
train_lower = train_mean - cut_off
train_upper = train_mean + cut_off

# Subset train data

test = pd.read_csv('test_csv')

# Subset test data
test = test[(test[['col']] < train_upper) & 
              (test[['col']] > train_lower)]

Mengapa hanya gunakan data pelatihan?

Kebocoran data: Menggunakan data yang tidak akan tersedia saat menilai kinerja model Anda

Hindari kebocoran data!

Rekayasa Fitur untuk Machine Learning di Python