为了尝试使用Python进行一些练习,我为自己分配了许多机器学习统计任务。目前,我正努力为Logistic回归编码交叉验证。
以下是一些代码,它们产生了我正在处理的综合数据集:
#### Create synthetic data
import pandas as pd
from pandas import DataFrame
import numpy as np
import random
from scipy.stats import bernoulli
from sklearn import preprocessing
customerID, sex, age, salary, happiness = [], [], [], [], []
random.seed(45)
for i in range(0,60):
customerID.append(i+1)
age.append(random.randint(18,65))
salary.append(random.randint(1200,3600))
if i%2==0:
sex.append('M')
else:
sex.append('F')
if salary[i]>=120*age[i] and sex[i]=='M':
p = 0.75
elif salary[i]>=120*age[i] and sex[i]=='F':
p = 0.7
elif salary[i]<=70*age[i] and sex[i]=='M':
p = 0.4
elif salary[i]<=70*age[i] and sex[i]=='F':
p = 0.5
else:
p = 0.58
happiness.append(-1+bernoulli.rvs(p,1))
### Create dataFrame now
df = pd.DataFrame(list(zip(customerID,sex,age,salary,happiness)),
columns =['customerID','sex','age','salary','happiness'])
le = preprocessing.LabelEncoder()
for column_name in df.columns:
if df[column_name].dtype == object:
df[column_name] = le.fit_transform(df[column_name])
else:
pass
df.head()
# Divide the data into dependent variable and independent variables
X = pd.DataFrame(df.iloc[:,[0,1,2,3]])
y = pd.DataFrame(df.iloc[:,[4]])
这是产生“ IndexError:数组的索引过多”的代码:
from sklearn.linear_model import LogisticRegression
from sklearn import metrics, cross_validation
from sklearn import metrics, cross_validation
logreg=LogisticRegression()
predicted = cross_validation.cross_val_predict(logreg, X, y, cv=10)
print(metrics.accuracy_score(y, predicted))
print(metrics.classification_report(y, predicted))
您将如何解决这个问题?
答案 0 :(得分:0)
我刚刚意识到替换
predicted = cross_validation.cross_val_predict(logreg, X, y, cv=10)
与
predicted = cross_validation.cross_val_predict(logreg, X, y.values.ravel(), cv=10)
工作正常。