title: Grandient Boosting Regressor : "TypeError: cannot perform reduce with flexible type"
body: <p>I am new in python and I am trying to developp a program with Gradient Boosting Regressor.
  I have two big sets of data, one training set and one test set in wich I have exactly the same columns. My goal is to predict the SeriousDlqin2yrs column of the test set with the information of the training set.</p>
  
  <p>This is the program I wrote : </p>
  
  <pre><code>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))
  </code></pre>
  
  <p>But when I run the program, python give me this answer : </p>
  
  <pre><code>Traceback (most recent call last):
    File "C:\Users\Paul HONORE\Dropbox\Research Study\Kaggle\Bank\GradientBoostedRegression1.1.py", line 64, in &lt;module&gt;
      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
  </code></pre>
  
  <p>I don't know from where it comes, i read lots of paper about this question but never found a solution for this particular problem.</p>
  
  <p>Thank you in advance for your help.</p>