我是 python 新手，我正在尝试使用 Gradient Boosting Regressor 开发一个程序。我有两大组数据，一个训练集和一个测试集，其中我有完全相同的列。我的目标是用训练集的信息来预测测试集的 SeriousDlqin2yrs 列。
这是我写的程序：
import numpy as np
import csv as csv
import pandas as pd
from sklearn import ensemble
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.utils import shuffle
# Load data
csv_file_object = csv.reader(open('cs-training-cleandata2NOLOG.csv', 'rb')) #Load in the training csv file
header = csv_file_object.next() #Skip the fist line as it is a header
train_data=[] #Creat a variable called 'train_data'
for row in csv_file_object: #Skip through each row in the csv file
train_data.append(row[1:]) #adding each row to the data variable
train_data = np.array(train_data) #Then convert from a list to an array
test_file_object = csv.reader(open('cs-test-cleandata2NOLOG.csv', 'rb')) #Load in the test csv file
header = test_file_object.next() #Skip the fist line as it is a header
test_data=[] #Creat a variable called 'test_data'
ids = []
for row in test_file_object: #Skip through each row in the csv file
ids.append(row[0])
test_data.append(row[1:]) #adding each row to the data variable
test_data = np.array(test_data) #Then convert from a list to an array
test_data = np.delete(test_data,[0],1) #remove SeriousDlqin2yrs
print 'Training '
# Fit regression model
clf = GradientBoostingRegressor(n_estimators=1000, min_samples_split=100, learning_rate=0.01)
clf = clf.fit(train_data[0::,1::],train_data[0::,0])
print 'Predicting'
output=clf.predict(test_data)
open_file_object = csv.writer(open("GradientBoostedRegression1.1.csv", "wb"))
open_file_object.writerow(["Id","Probability"])
open_file_object.writerows(zip(ids, output))
但是当我运行程序时，python 给了我这个答案：
Traceback (most recent call last):
File "C:\Users\Paul HONORE\Dropbox\Research Study\Kaggle\Bank\GradientBoostedRegression1.1.py", line 64, in <module>
clf = clf.fit(train_data[0::,1::],train_data[0::,0])
File "C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line 1126, in fit
return super(GradientBoostingRegressor, self).fit(X, y)
File "C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line 595, in fit
self.init_.fit(X, y)
File "C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line 69, in fit
self.mean = np.mean(y)
File "C:\Python27\lib\site-packages\numpy\core\fromnumeric.py", line 2716, in mean
out=out, keepdims=keepdims)
File "C:\Python27\lib\site-packages\numpy\core\_methods.py", line 62, in _mean
ret = um.add.reduce(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
TypeError: cannot perform reduce with flexible type
我不知道它来自哪里，我阅读了很多关于这个问题的论文，但从未找到解决这个特定问题的方法。
预先感谢您的帮助。