decisiontreeregressor_决策树建树及参数调优策略实战

%matplotlib inline

import matplotlib.pyplot as plt

import pandas as pd

#引入数据

from sklearn.datasets.california_housing import fetch_california_housing

housing = fetch_california_housing()

print(housing.DESCR)

#导入sklearn 建树包 fit(x,y) x:训练样本 y:记录样本标签

from sklearn import tree

dtr = tree.DecisionTreeRegressor(max_depth=2)

dtr.fit(housing.data[:,[6,7]],housing.target)

可视化

#先安装Graphviz 官网下就行

dot_data =

tree.export_graphviz(

dtr,

out_file = None,

feature_names = housing.feature_names[6:8],

filled = True,

impurity = False,

rounded = True

)

import os

os.environ["PATH"] += os.pathsep + C:Program Files (x86)Graphviz2.38in

#指定图像模板

import pydotplus

graph = pydotplus.graph_from_dot_data(dot_data)

graph.get_nodes()[7].set_fillcolor("#FFF2DD")

from IPython.display import Image

Image(graph.create_png())

生成树图：

from sklearn.model_selection import train_test_split

data_train,data_test,target_train,target_test =

train_test_split(housing.data,housing.target,test_size=0.1,random_state =42)

dtr = tree.DecisionTreeRegressor(random_state=42)

dtr.fit(data_train,target_train)

dtr.score(data_test,target_test)

from sklearn.ensemble import RandomForestRegressor

rfr = RandomForestRegressor(random_state =42)

rfr.fit(data_train,target_train)

rfr.score(data_test,target_test)

#参数调优，交叉验证

from sklearn.model_selection import GridSearchCV

tree_param_grid = {min_samples_split:list((3,6,9)),n_estimators:list((10,50,100))}

grid = GridSearchCV(RandomForestRegressor(),param_grid = tree_param_grid,cv = 3)

grid.fit(data_train,target_train)

grid.grid_scores_ ,grid.best_params_ ,grid.best_score_

%matplotlib inline import matplotlib.pyplot as plt import pandas as pd #引入数据 from sklearn.datasets.california_housing import fetch_california_housing housing = fetch_california_housing() print(housing.DESCR) #导入sklearn 建树包 fit(x,y) x:训练样本 y:记录样本标签 from sklearn import tree dtr = tree.DecisionTreeRegressor(max_depth=2) dtr.fit(housing.data[:,[6,7]],housing.target) 可视化 #先安装Graphviz 官网下就行 dot_data = tree.export_graphviz( dtr, out_file = None, feature_names = housing.feature_names[6:8], filled = True, impurity = False, rounded = True ) import os os.environ["PATH"] += os.pathsep + C:Program Files (x86)Graphviz2.38in #指定图像模板 import pydotplus graph = pydotplus.graph_from_dot_data(dot_data) graph.get_nodes()[7].set_fillcolor("#FFF2DD") from IPython.display import Image Image(graph.create_png()) 生成树图： from sklearn.model_selection import train_test_split data_train,data_test,target_train,target_test = train_test_split(housing.data,housing.target,test_size=0.1,random_state =42) dtr = tree.DecisionTreeRegressor(random_state=42) dtr.fit(data_train,target_train) dtr.score(data_test,target_test) from sklearn.ensemble import RandomForestRegressor rfr = RandomForestRegressor(random_state =42) rfr.fit(data_train,target_train) rfr.score(data_test,target_test) #参数调优，交叉验证 from sklearn.model_selection import GridSearchCV tree_param_grid = {min_samples_split:list((3,6,9)),n_estimators:list((10,50,100))} grid = GridSearchCV(RandomForestRegressor(),param_grid = tree_param_grid,cv = 3) grid.fit(data_train,target_train) grid.grid_scores_ ,grid.best_params_ ,grid.best_score_