# 应用python随机森林回归模型训练好模型后，如何进行预测？为什么预测值会这么小，且出现多个相同的异常值？

• 1
``````

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
import numpy as np

#随机森林回归

# 1 准备数据
# 读取波士顿地区房价信息
#print("boston:", boston)
# 查看数据描述
# print(boston.DESCR)   # 共506条波士顿地区房价信息，每条13项数值特征描述和目标房价
# 查看数据的差异情况
# print("最大房价：", np.max(boston.target))   # 50
# print("最小房价：",np.min(boston.target))    # 5
# print("平均房价：", np.mean(boston.target))   # 22.532806324110677

x = boston.data
y = boston.target
print("x.shape:", x.shape)
print("y.shape:", y.shape)
# 2 分割训练数据和测试数据
# 随机采样25%作为测试 75%作为训练
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)
print("x_train.shape:", x_train.shape)
print("x_test.shape:", x_test.shape)
print("y_train.shape:", y_train.shape)
print("y_test.shape:", y_test.shape)
# 3 训练数据和测试数据进行标准化处理
ss_x = StandardScaler()
x_train = ss_x.fit_transform(x_train)
x_test = ss_x.transform(x_test)

ss_y = StandardScaler()
y_train = ss_y.fit_transform(y_train.reshape(-1, 1))
y_test = ss_y.transform(y_test.reshape(-1, 1))

# 随机森林回归
rfr = RandomForestRegressor()
# 训练
rfr.fit(x_train, y_train)
# 预测 保存预测结果
rfr_y_predict = rfr.predict(x_test)
#对所有特征数据进行预测
Y_predict=rfr.predict(x)

# 随机森林回归模型评估
print("随机森林回归的默认评估值为：", rfr.score(x_test, y_test))
print("随机森林回归的R_squared值为：", r2_score(y_test, rfr_y_predict))
print("随机森林回归的均方误差为:", mean_squared_error(ss_y.inverse_transform(y_test),
ss_y.inverse_transform(rfr_y_predict)))
print("随机森林回归的平均绝对误差为:", mean_absolute_error(ss_y.inverse_transform(y_test),
ss_y.inverse_transform(rfr_y_predict)))
print(y)
print(Y_predict)

#输出的结果
x.shape: (506, 13)
y.shape: (506,)
x_train.shape: (379, 13)
x_test.shape: (127, 13)
y_train.shape: (379,)
y_test.shape: (127,)

[24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4
18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8
18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6
25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4
24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9
24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9
23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7
43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8
18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4
15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8
14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4
17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8
23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2
37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.
33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.
21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1
44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5
23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8
29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8
30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1
45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9
21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2
22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1
20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1
19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6
22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8
21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3
13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2
9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.
11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4
16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3
11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6
14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7
19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3
16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.
8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9
22.  11.9]
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1.16610311 1.16610311 1.17353858 1.16610311 1.16610311 1.16610311
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1.17246098 1.17246098 1.17246098 1.17246098 1.17246098 1.17246098
1.17246098 1.17246098 1.17246098 1.17246098 1.17246098 1.17246098
1.17246098 1.18884057 1.18884057 1.18884057 1.18884057 1.18884057
1.18884057 1.18884057 1.18884057 1.22849641 1.23140594 1.23140594
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1.18884057 1.17246098 1.16771952 1.16771952 1.17246098 1.21017713
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1.17246098 1.17246098 1.17246098 1.17246098 1.17774124 1.17774124
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1.22655672 1.18743968 1.18743968 1.18743968 1.18884057 1.18743968
1.18884057 1.22655672 1.18884057 1.18884057 1.18884057 1.20888401
1.20888401 1.20888401 1.20888401 1.20888401 1.20888401 1.20220286
1.20888401 1.20414255 1.20241838 1.20888401 1.20888401 1.18884057
1.22655672 1.18743968 1.18884057 1.18743968 1.18884057 1.18884057
1.18884057 1.18884057 1.18884057 1.18743968 1.18884057 1.18884057
1.18884057 1.22838865 1.18884057 1.23140594 1.23140594 1.18884057
1.18884057 1.18884057 1.18884057 1.18884057 1.18884057 1.17989645
1.17989645 1.17989645 1.17989645 1.17989645 1.23140594 1.23140594
1.23140594 1.18884057 1.18884057 1.18884057 1.23140594 1.23140594
1.23140594 1.23140594 1.17774124 1.17774124 1.17763348 1.19185786
1.16771952 1.16771952 1.17774124 1.16771952 1.17774124 1.16771952
1.17774124 1.17774124 1.17106009 1.17106009 1.21017713 1.18323702
1.21017713 1.17106009 1.21017713 1.16771952 1.17989645 1.17989645
1.17989645 1.18884057 1.18884057 1.17246098 1.17246098 1.17246098
1.17246098 1.17246098 1.17246098 1.17246098 1.17246098 1.23140594
1.21502635 1.18140509 1.18140509 1.17989645 1.17989645 1.18884057
1.23140594 1.22838865 1.22838865 1.23140594 1.23140594 1.23140594
1.23140594 1.23140594 1.16567207 1.16384014 1.159853   1.159853
1.159853   1.16384014 1.16384014 1.159853   1.16384014 1.159853
1.16384014 1.16567207 1.159853   1.159853   1.15058561 1.16125389
1.15543483 1.1644867  1.16125389 1.16567207 1.16567207 1.16567207
1.16567207 1.16567207 1.16567207 1.16567207 1.16567207 1.159853
1.16567207 1.16567207 1.16567207 1.16567207 1.16567207 1.159853
1.159853   1.159853   1.16567207 1.159853   1.16567207 1.159853
1.159853   1.159853   1.16567207 1.16567207 1.16567207 1.16567207
1.16567207 1.16567207 1.16567207 1.16567207 1.1644867  1.16567207
1.159853   1.16567207 1.16567207 1.16567207 1.16567207 1.16567207
1.16567207 1.16567207 1.16567207 1.16567207 1.16567207 1.16567207
1.16567207 1.159853   1.16567207 1.159853   1.159853   1.16567207
1.16567207 1.16567207 1.159853   1.16567207 1.159853   1.16567207
1.159853   1.159853   1.16567207 1.16567207 1.16567207 1.16567207
1.16567207 1.16567207 1.16567207 1.16567207 1.159853   1.16567207
1.16567207 1.16567207 1.159853   1.16567207 1.16567207 1.159853
1.26653585 1.159853   1.159853   1.159853   1.16567207 1.159853
1.159853   1.159853   1.159853   1.159853   1.159853   1.16384014
1.159853   1.159853   1.159853   1.17763348 1.16384014 1.159853
1.16567207 1.16567207 1.159853   1.159853   1.16384014 1.159853
1.159853   1.159853   1.159853   1.16567207 1.16567207 1.16567207
1.159853   1.159853   1.159853   1.17763348 1.17763348 1.16384014
1.159853   1.159853   1.17246098 1.17106009 1.17106009 1.17246098
1.17246098 1.17106009 1.15080113 1.17106009 1.16987473 1.16771952
1.16771952 1.17106009 1.17106009 1.17246098 1.17246098 1.17246098
1.17246098 1.17246098]``````

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