sklearn解決迴歸問題

這裏只是大致統計一下利用sklearn做迴歸的方法選擇， 後續進行案例分析。

方法：

# 線性迴歸
from sklearn.linear_model import LinearRegression
linear_regression = LinearRegression()

# 決策樹迴歸
from sklearn import tree
decision_tree_regression = tree.DecisionTreeRegressor()

# 支持向量機迴歸
from sklearn import svm
svm = svm.SVR()

# K近鄰迴歸
from sklearn import neighbors
k_neighbor = neighbors.KNeighborsRegressor()

# 隨機森林迴歸
from sklearn import ensemble
random_forest_regressor = ensemble.RandomForestRegressor(n_estimators=20) 

# Adaboost迴歸
from sklearn import ensemble
adaboost_regressor = ensemble.AdaBoostRegressor(n_estimators=50)

# GBRT迴歸
from sklearn import ensemble
gradient_boosting_regressor = ensemble.GradientBoostingRegressor(n_estimators=100) 

# Bagging迴歸
from sklearn import ensemble
bagging_regressor = ensemble.BaggingRegressor()

# ExtraTree極端隨機數迴歸
from sklearn.tree import ExtraTreeRegressor
extra_tree_regressor = ExtraTreeRegressor()