输出结果

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)

设计思路

核心代码

linear_svr = SVR(kernel='linear')

linear_svr.fit(X_train, y_train)

linear_svr_y_predict = linear_svr.predict(X_test)

linear_svr.score(X_test, y_test)

r2_score(y_test, linear_svr_y_predict))

mean_squared_error(ss_y.inverse_transform(y_test)

ss_y.inverse_transform(linear_svr_y_predict)

mean_absolute_error(ss_y.inverse_transform(y_test)

ss_y.inverse_transform(linear_svr_y_predict)

poly_svr.score(X_test, y_test)

r2_score(y_test, poly_svr_y_predict)

mean_squared_error(ss_y.inverse_transform(y_test)

ss_y.inverse_transform(poly_svr_y_predict)