基于sklearn的几种回归模型理论代码实现

from sklearn.datasets import load_boston
boston = load_boston()
print(boston.DESCR)

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)

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(boston.data,boston.target,random_state=33,test_size=0.25)
print(x_test.shape)

(127, 13)    

from sklearn.preprocessing import StandardScaler
ss_x,ss_y = StandardScaler(),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])).reshape(-1)
y_test = ss_y.transform(y_test.reshape([-1,1])).reshape(-1)
print(y_train.shape)

(379,)

from sklearn.svm import SVR

l_svr = SVR(kernel='linear')
l_svr.fit(x_train,y_train)
l_svr.score(x_test,y_test)

0.65171709742960804

n_svr = SVR(kernel="poly")
n_svr.fit(x_train,y_train)
n_svr.score(x_test,y_test)

0.40445405800289286

r_svr = SVR(kernel="rbf")
r_svr.fit(x_train,y_train)
r_svr.score(x_test,y_test)

0.75640689122739346

from sklearn.neighbors import KNeighborsRegressor
knn = KNeighborsRegressor(weights="uniform")
knn.fit(x_train,y_train)
knn.score(x_test,y_test)

0.69034545646065615

from sklearn.tree import DecisionTreeRegressor
dt = DecisionTreeRegressor()
dt.fit(x_train,y_train)
dt.score(x_test,y_test)

0.68783308418825428

from sklearn.ensemble import RandomForestRegressor
rfr = RandomForestRegressor()
rfr.fit(x_train,y_train)
rfr.score(x_test,y_test)

0.79055864833158895

from sklearn.ensemble import ExtraTreesRegressor
etr = ExtraTreesRegressor()
etr.fit(x_train,y_train)
etr.score(x_test,y_test)

0.7349024110033624

from sklearn.ensemble import GradientBoostingRegressor
gbr = GradientBoostingRegressor()
gbr.fit(x_train,y_train)
gbr.score(x_test,y_test)

0.84501318676123161