ML之kNN(两种)：基于两种kNN(平均回归、加权回归)对Boston(波士顿房价)数据集(506,13+1)进行价格回归预测并对比各自性能

ML之kNN(两种)：基于两种kNN(平均回归、加权回归)对Boston(波士顿房价)数据集(506,13+1)进行价格回归预测并对比各自性能

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输出结果

Boston House Prices dataset
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Notes
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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

uni_knr.fit(X_train, y_train)
uni_knr_y_predict = uni_knr.predict(X_test)

dis_knr.fit(X_train, y_train)
dis_knr_y_predict = dis_knr.predict(X_test)

uni_knr.score(X_test, y_test)
r2_score(y_test,uni_knr_y_predict)
mean_squared_error(ss_y.inverse_transform(y_test)
ss_y.inverse_transform(uni_knr_y_predict)
mean_absolute_error(ss_y.inverse_transform(y_test)
ss_y.inverse_transform(uni_knr_y_predict)

r2_score(y_test,dis_knr_y_predict)
mean_squared_error(ss_y.inverse_transform(y_test)
ss_y.inverse_transform(dis_knr_y_predict)
mean_absolute_error(ss_y.inverse_transform(y_test)
ss_y.inverse_transform(dis_knr_y_predict)