[sklearn] (2) preprocessing 데이터 전처리 모듈

 

 

preprocessing 데이터 전처리 모듈

• 데이터의 특징 스케일링(feature scaling)을 위한 방법으로 표준화(Standardization)와 정규화(Normalization) 사용
• 표준화 방법

• 정규화 방법

• scikit-learn에서는 개별 벡터 크기를 맞추는 형태로 정규화

 

 

StandardScaler: 표준화 클래스

 

In [37]:

iris = load_iris()
iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
iris_df.describe()

 

Out[37]:

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
count	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.057333	3.758000	1.199333
std	0.828066	0.435866	1.765298	0.762238
min	4.300000	2.000000	1.000000	0.100000
25%	5.100000	2.800000	1.600000	0.300000
50%	5.800000	3.000000	4.350000	1.300000
75%	6.400000	3.300000	5.100000	1.800000
max	7.900000	4.400000	6.900000	2.500000

 

In [42]:

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
iris_scaled = scaler.fit_transform(iris_df)
iris_df_scaled = pd.DataFrame(data=iris_scaled, columns=iris.feature_names)
iris_df.describe()

 

Out[42]:

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
count	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.057333	3.758000	1.199333
std	0.828066	0.435866	1.765298	0.762238
min	4.300000	2.000000	1.000000	0.100000
25%	5.100000	2.800000	1.600000	0.300000
50%	5.800000	3.000000	4.350000	1.300000
75%	6.400000	3.300000	5.100000	1.800000
max	7.900000	4.400000	6.900000	2.500000

 

In [44]:

X_train, X_test, y_train, y_test = train_test_split(iris_df_scaled, iris.target, test_size=0.3)

model = LogisticRegression()
model.fit(X_train, y_train)

print('훈련 데이터 점수: {}'.format(model.score(X_train, y_train)))
print('평가 데이터 점수: {}'.format(model.score(X_test, y_test)))

훈련 데이터 점수: 0.9809523809523809
평가 데이터 점수: 0.9111111111111111

 

 

MinMaxScaler: 정규화 클래스

 

In [49]:

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
iris_scaled = scaler.fit_transform(iris_df)
iris_df_scaled = pd.DataFrame(data=iris_scaled, columns=iris.feature_names)
iris_df_scaled.describe()

 

Out[49]:

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
count	150.000000	150.000000	150.000000	150.000000
mean	0.428704	0.440556	0.467458	0.458056
std	0.230018	0.181611	0.299203	0.317599
min	0.000000	0.000000	0.000000	0.000000
25%	0.222222	0.333333	0.101695	0.083333
50%	0.416667	0.416667	0.567797	0.500000
75%	0.583333	0.541667	0.694915	0.708333
max	1.000000	1.000000	1.000000	1.000000

 

In [51]:

X_train, X_test, y_train, y_test = train_test_split(iris_df_scaled, iris.target, test_size=0.3)
model = LogisticRegression()
model.fit(X_train, y_train)

print('훈련 데이터 점수: {}'.format(model.score(X_train, y_train)))
print('평가 데이터 점수: {}'.format(model.score(X_test, y_test)))

 

훈련 데이터 점수: 0.9238095238095239
평가 데이터 점수: 0.8888888888888888

 

 

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