使用變換來提公升單回歸準確度的乙個反例

import

numpy as np
import
matplotlib.pyplot as plt
from sklearn.linear_model import
ridge
from sklearn.metrics import
r2_score, mean_squared_error
from sklearn.neighbors import
kneighborsregressor
from sklearn.preprocessing import
standardscaler
x_train =np.array([
[158, 1],
[170, 1],
[183, 1],
[191, 1],
[155, 0],
[163, 0],
[180, 0],
[158, 0],
[170, 0]
])ss =standardscaler()
x_trainss =ss.fit_transform(x_train)
x_train_log = np.log(x_train + 1)
y_train = [64, 86, 84, 80, 49, 59, 67, 54, 67]
x_test =np.array([
[160, 1],
[196, 1],
[168, 0],
[177, 0]
])x_testss =ss.transform(x_test)
x_test_log = np.log(x_test + 1)
y_test = [66, 87, 68, 74]
k = 5clf = kneighborsregressor(n_neighbors=k)
clf.fit(x_train, y_train)
clf1 = kneighborsregressor(n_neighbors=k)
clf1.fit(x_trainss, y_train)
clf2 =ridge().fit(x_train_log, y_train)
predictions =clf.predict(np.array(x_test))
predictions1 =clf1.predict(x_testss)
predictions2 =clf2.predict(x_test_log)
print('
actual weights: %s
' %y_test)
print('
predicted weights: %s
' %predictions)
print('
predicted weights standardscaler: %s
' %predictions1)
print('
predicted weights log: %s
' %predictions2)
print
(mean_squared_error(y_test, predictions))
print
(mean_squared_error(y_test, predictions1))
print
(mean_squared_error(y_test, predictions2))
print
(r2_score(y_test, predictions))
print
(r2_score(y_test, predictions1))
print(r2_score(y_test, predictions2))

結果是：

actual weights: [66, 87, 68, 74]

predicted weights: [62.4 76.8 66. 72.6]

predicted weights by standardscaler: [69.4 76.8 59.2 59.2]

predicted weights by log: [72.98731557 73.88528401 63.37281696 63.60369452]

mean_squared_error: 30.740000000000023

mean_squared_error by standardscaler: 103.02

mean_squared_error by log: 87.57808624078896

0.5424744186046508

-0.5333209302325581

-0.30348779521174274

process finished with exit code 0

我們發現特徵值經過標準化和對數化的轉換後，**均方差偏移反而更大了。本例來自《scikit-learn機器學習》的第三章的最後乙個例子的延展，所以書中的例子感覺還有缺陷，原因是樣本太少了。

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