sklearn回归

from numpy import *
import seaborn as sns
import pandas as pd
from sklearn.linear_model import *
from sklearn.pipeline import *
from sklearn.model_selection import train_test_split
from numpy import float64
from sklearn import *
from sklearn.ensemble import BaggingRegressor
from sklearn.tree import ExtraTreeRegressor
from sklearn.externals import joblib


def create_model():
　　dict = {} 
　　model_DecisionTreeRegressor = tree.DecisionTreeRegressor()
　　model_LinearRegression = linear_model.LinearRegression()
　　model_SVR = svm.SVR()
　　model_KNeighborsRegressor = neighbors.KNeighborsRegressor()
　　model_RandomForestRegressor = ensemble.RandomForestRegressor(n_estimators=20)
　　model_AdaBoostRegressor = ensemble.AdaBoostRegressor(n_estimators=50)
　　model_GradientBoostingRegressor = ensemble.GradientBoostingRegressor(n_estimators=100)
　　model_BaggingRegressor = BaggingRegressor()
　　model_ExtraTreeRegressor = ExtraTreeRegressor()

　　dict['DecisionTreeRegressor'] = model_DecisionTreeRegressor
　　dict['LinearRegression'] = model_LinearRegression
　　dict['SVR'] = model_SVR
　　dict['KNeighborsRegressor'] = model_KNeighborsRegressor
　　dict['RandomForestRegressor'] = model_RandomForestRegressor
　　dict['AdaBoostRegressor'] = model_AdaBoostRegressor
　　dict['GradientBoostingRegressor'] = model_GradientBoostingRegressor
　　dict['BaggingRegressor'] = model_BaggingRegressor
　　dict['ExtraTreeRegressor'] = model_ExtraTreeRegressor

　　return dict