from sklearn.datasets import load_boston
boston = load_boston()
print(boston.DESCR)
import numpy as pd
x = boston.data
y = boston.target
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)
#從sklearn.preprocessing導入數據標準化模塊
from sklearn.preprocessing import StandardScaler
ss_x = StandardScaler()
ss_y = StandardScaler()
x_train = ss_x.fit_transform(x_train)
x_test = ss_x.transform(x_test)
y_train = ss_y.fit_transform(y_train.reshape(-1, 1))
y_test = ss_y.transform(y_test.reshape(-1, 1))
from sklearn.svm import SVR
linear_svr = SVR(kernel='linear')
linear_svr.fit(x_train, y_train.ravel())
linear_svr_predict = linear_svr.predict(x_test)
poly_svr = SVR(kernel='poly')
poly_svr.fit(x_train, y_train.ravel())
poly_svr_predict = poly_svr.predict(x_test)
rbf_svr = SVR(kernel='rbf')
rbf_svr.fit(x_train, y_train.ravel())
rbf_svr_predict = rbf_svr.predict(x_test)
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
print('The value of default measurement of linear SVR is', linear_svr.score(x_test, y_test))
print('R-squared value of linear SVR is', r2_score(y_test, linear_svr_predict))
print('The mean squared error of linear SVR is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(linear_svr_predict)))
print('The mean absolute error of linear SVR is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(linear_svr_predict)))
print('\nThe value of default measurement of poly SVR is', poly_svr.score(x_test, y_test))
print('R-squared value of poly SVR is', r2_score(y_test, poly_svr_predict))
print('The mean squared error of poly SVR is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(poly_svr_predict)))
print('The mean absolute error of poly SVR is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(poly_svr_predict)))
print('\nThe value of default measurement of rbf SVR is', rbf_svr.score(x_test, y_test))
print('R-squared value of rbf SVR is', r2_score(y_test, rbf_svr_predict))
print('The mean squared error of rbf SVR is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rbf_svr_predict)))
print('The mean absolute error of rbf SVR is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rbf_svr_predict)))
最終的運行結果如下：
Boston House Prices dataset
===========================
Notes
------
Data Set Characteristics:
:Number of Instances: 506
:Number of Attributes: 13 numeric/categorical predictive
:Median Value (attribute 14) is usually the target
:Attribute Information (in order):
- CRIM per capita crime rate by town
- ZN proportion of residential land zoned for lots over 25,000 sq.ft.
- INDUS proportion of non-retail business acres per town
- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
- NOX nitric oxides concentration (parts per 10 million)
- RM average number of rooms per dwelling
- AGE proportion of owner-occupied units built prior to 1940
- DIS weighted distances to five Boston employment centres
- RAD index of accessibility to radial highways
- TAX full-value property-tax rate per $10,000
- PTRATIO pupil-teacher ratio by town
- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
- LSTAT % lower status of the population
- MEDV Median value of owner-occupied homes in $1000's
:Missing Attribute Values: None
:Creator: Harrison, D. and Rubinfeld, D.L.
This is a copy of UCI ML housing dataset.
http://archive.ics.uci.edu/ml/datasets/Housing
This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.
The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
prices and the demand for clean air', J. Environ. Economics & Management,
vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics
...', Wiley, 1980. N.B. Various transformations are used in the table on
pages 244-261 of the latter.
The Boston house-price data has been used in many machine learning papers that address regression
problems.
**References**
- Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
- Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.
- many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)
The value of default measurement of linear SVR is 0.65171709743
R-squared value of linear SVR is 0.65171709743
The mean squared error of linear SVR is 27.0063071393
The mean absolute error of linear SVR is 3.42667291687
The value of default measurement of poly SVR is 0.404454058003
R-squared value of poly SVR is 0.404454058003
The mean squared error of poly SVR is 46.179403314
The mean absolute error of poly SVR is 3.75205926674
The value of default measurement of rbf SVR is 0.756406891227
R-squared value of rbf SVR is 0.756406891227
The mean squared error of rbf SVR is 18.8885250008
The mean absolute error of rbf SVR is 2.60756329798
