【優達學城測評】sklearn-線性迴歸

我的以爲這是最最簡單的，套直線方程就能夠。

同時，這個擬合的代碼彷佛在後續監督學習中能夠套用，未完待續。先貼這段代碼。

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)

打印出的預測結果並不精確，因此後續須要學習迴歸會出現的偏差類型，以及R平方指標，若是過分擬合，那麼這個指標就會很低

 

#!/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())