下面给出了使用Scikit-learn在波士顿房屋定价数据集上实现多元线性回归技术。

import matplotlib.pyplot as plt

import numpy as np

from sklearn import datasets, linear_model, metrics

# load the boston dataset

boston = datasets.load_boston(return_X_y=False)

# defining feature matrix(X) and response vector(y)

X = boston.data

y = boston.target

# splitting X and y into training and testing sets

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4,

random_state=1)

# create linear regression object

reg = linear_model.LinearRegression()

# train the model using the training sets

reg.fit(X_train, y_train)

# regression coefficients

print('Coefficients: \n', reg.coef_)

# variance score: 1 means perfect prediction

print('Variance score: {}'.format(reg.score(X_test, y_test)))

# plot for residual error

## setting plot style

plt.style.use('fivethirtyeight')

## plotting residual errors in training data

plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train,

color = "green", s = 10, label = 'Train data')

## plotting residual errors in test data

plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test,

color = "blue", s = 10, label = 'Test data')

## plotting line for zero residual error

plt.hlines(y = 0, xmin = 0, xmax = 50, linewidth = 2)

## plotting legend

plt.legend(loc = 'upper right')

## plot title

plt.title("Residual errors")

## function to show plot

plt.show()

上述程序的输出如下：

系数：
[-8.80740828e-02 6.72507352e-02 5.10280463e-02 2.18879172e + 00
-1.72283734e + 01 3.62985243e + 00 2.13933641e-03 -1.36531300e + 00
2.88788067e-01 -1.22618657e-02 -8.36014969e-01 9.53058061e-03
-5.05036163e-01]
差异分数：0.720898784611