版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/mr_muli/article/details/84796955
- 机器学习之随机森林RandomForestRegressor
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 4 18:29:45 2018
@author: muli
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets,cross_validation,ensemble
def load_data_regression():
'''
加载用于回归问题的数据集
:return: 一个元组，用于回归问题。元组元素依次为：训练样本集、测试样本集、训练样本集对应的值、测试样本集对应的值
'''
diabetes = datasets.load_diabetes() #使用 scikit-learn 自带的一个糖尿病病人的数据集
return cross_validation.train_test_split(diabetes.data,diabetes.target,
test_size=0.25,random_state=0) # 拆分成训练集和测试集，测试集大小为原始数据集大小的 1/4
def test_RandomForestRegressor(*data):
'''
测试 RandomForestRegressor 的用法
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
:return: None
'''
X_train,X_test,y_train,y_test=data
regr=ensemble.RandomForestRegressor()
regr.fit(X_train,y_train)
print("Traing Score:%f"%regr.score(X_train,y_train))
print("Testing Score:%f"%regr.score(X_test,y_test))
def test_RandomForestRegressor_num(*data):
'''
测试 RandomForestRegressor 的预测性能随 n_estimators 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
:return: None
'''
X_train,X_test,y_train,y_test=data
nums=np.arange(1,100,step=2)
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
testing_scores=[]
training_scores=[]
for num in nums:
regr=ensemble.RandomForestRegressor(n_estimators=num)
regr.fit(X_train,y_train)
training_scores.append(regr.score(X_train,y_train))
testing_scores.append(regr.score(X_test,y_test))
ax.plot(nums,training_scores,label="Training Score")
ax.plot(nums,testing_scores,label="Testing Score")
ax.set_xlabel("estimator num")
ax.set_ylabel("score")
ax.legend(loc="lower right")
ax.set_ylim(-1,1)
plt.suptitle("RandomForestRegressor")
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
plt.show()
def test_RandomForestRegressor_max_depth(*data):
'''
测试 RandomForestRegressor 的预测性能随 max_depth 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
:return: None
'''
X_train,X_test,y_train,y_test=data
maxdepths=range(1,20)
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
testing_scores=[]
training_scores=[]
for max_depth in maxdepths:
regr=ensemble.RandomForestRegressor(max_depth=max_depth)
regr.fit(X_train,y_train)
training_scores.append(regr.score(X_train,y_train))
testing_scores.append(regr.score(X_test,y_test))
ax.plot(maxdepths,training_scores,label="Training Score")
ax.plot(maxdepths,testing_scores,label="Testing Score")
ax.set_xlabel("max_depth")
ax.set_ylabel("score")
ax.legend(loc="lower right")
ax.set_ylim(0,1.05)
plt.suptitle("RandomForestRegressor")
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
plt.show()
def test_RandomForestRegressor_max_features(*data):
'''
测试 RandomForestRegressor 的预测性能随 max_features 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
:return: None
'''
X_train,X_test,y_train,y_test=data
max_features=np.linspace(0.01,1.0)
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
testing_scores=[]
training_scores=[]
for max_feature in max_features:
regr=ensemble.RandomForestRegressor(max_features=max_feature)
regr.fit(X_train,y_train)
training_scores.append(regr.score(X_train,y_train))
testing_scores.append(regr.score(X_test,y_test))
ax.plot(max_features,training_scores,label="Training Score")
ax.plot(max_features,testing_scores,label="Testing Score")
ax.set_xlabel("max_feature")
ax.set_ylabel("score")
ax.legend(loc="lower right")
ax.set_ylim(0,1.05)
plt.suptitle("RandomForestRegressor")
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
plt.show()
if __name__=='__main__':
X_train,X_test,y_train,y_test=load_data_regression() # 获取回归数据
# test_RandomForestRegressor(X_train,X_test,y_train,y_test) # 调用 test_RandomForestRegressor
# test_RandomForestRegressor_num(X_train,X_test,y_train,y_test) # 调用 test_RandomForestRegressor_num
# test_RandomForestRegressor_max_depth(X_train,X_test,y_train,y_test) # 调用 test_RandomForestRegressor_max_depth
test_RandomForestRegressor_max_features(X_train,X_test,y_train,y_test) # 调用 test_RandomForestRegressor_max_features
- 如图：
