基于scikit-learn的简单逻辑回归（Logistic regression）练习

#用逻辑回归解决该问题
from sklearn.linear_model import LogisticRegression as LR
from sklearn.linear_model import RandomizedLogisticRegression as RLR
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

X = train_data
Y = target

#无需进行分组，数据自行划分为测试集和训练集
# X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)

#归一化
sc=StandardScaler().fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

lr = LR()
lr.fit(X_train,Y_train)

pred_test = lr.predict_proba(X_test)
acc=lr.score(X_test,Y_test)

# result.to_csv('submission_result')