[Sklearn] 데이터 셋 (dataset)

데이터 셋 (dataset)

DESCR: dataset 정보

data: feature data

feature_names: feature data의 컬럼 이름

target: label data (수치형)

target_names: label의 이름 (문자형)

from sklearn.datasets import load_iris
iris = load_iris()

data = iris['data']
feature_names = iris['feature_names']
target = iris['target']

 

 

데이터프레임 생성

df_iris = pd.DataFrame(data, columns=feature_names)
df_iris['target'] = target

 

 

train / validation 세트 나누기

from sklearn.model_selection import train_test_split
x_train, x_valid, y_train, y_valid = train_test_split(df_iris.drop('target', 1), df_iris['target'])

x_train.shape, y_train.shape  # ((112, 4), (112,))
x_valid.shape, y_valid.shape  # ((38, 4), (38,))

 

 

 

stratify: label 클래스의 분포를 균등하게 배분

x_train, x_valid, y_train, y_valid = train_test_split(df_iris.drop('target', 1), df_iris['target'], stratify=df_iris['target'])