个人觉得这是最最简单的，套直线方程就可以。
同时，这个拟合的代码似乎在后续监督学习中可以套用，未完待续。先贴这段代码。
def studentReg(ages_train, net_worths_train):
### import the sklearn regression module, create, and train your regression
### name your regression reg
### your code goes here!
from sklearn.linear_model import LinearRegression
reg=LinearRegression()
reg.fit(ages_train,net_worths_train)
return reg
print"Katie's net worth prediction:", reg.predict([27])
print"slope:",reg.coef_
print"intercept:",reg.intercept_
print"\n ##########stats on test dataset #########\n"
print"r-squared score:", reg.score(ages_test,net_worths_test)
print"\n ##########stats on training dataset ##########\n"
print"r-squared score:", reg.score(ages_train,net_worths_train)
打印出的预测结果并不精确，所以后续需要学习回归会出现的误差类型，以及R平方指标，如果过度拟合，那么这个指标就会很低
#!/usr/bin/python
import numpy
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from studentRegression import studentReg
from class_vis import prettyPicture, output_image
from ages_net_worths import ageNetWorthData
ages_train, ages_test, net_worths_train, net_worths_test = ageNetWorthData()
reg = studentReg(ages_train, net_worths_train)
plt.clf()
plt.scatter(ages_train, net_worths_train, color="b", label="train data")
plt.scatter(ages_test, net_worths_test, color="r", label="test data")
plt.plot(ages_test, reg.predict(ages_test), color="black")
plt.legend(loc=2)
plt.xlabel("ages")
plt.ylabel("net worths")
plt.savefig("test.png")
output_image("test.png", "png", open("test.png", "rb").read())
转载于:https://my.oschina.net/Bettyty/blog/754145