logistic案例

1、查看输出y分类是否均衡？

sns.countplot(train.target);

pyplot.xlabel('target');

pyplot.ylabel('Number of occurrences');

2、代码

#四要素

import pandas as pd 
import numpy as np

from matplotlib import pyplot
import seaborn as sns
%matplotlib inline

#加载数据

train = pd.read_csv(dpath +"Otto_train.csv")

#特征编码

y_train = train['target']
y_train = y_train.map(lambda s: s[6:])
y_train = y_train.map(lambda s: int(s)-1)

X_train = train.drop(["id", "target"], axis=1)

#数据标准化

from sklearn.preprocessing import StandardScaler

ss_X = StandardScaler()

X_train = ss_X.fit_transform(X_train)

#模型训练

#no1：普通的logisticRegression

from sklearn.linear_model import LogisticRegression
lr= LogisticRegression()

#no2：加正则的logisticRegression