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Err=[Err11,Err12,Err13,...,Err1n][Err21,Err22,Err23,...,Err2n][Err31,Err32,Err33,...,Err3n]...[Errn1,Errn2,Errn3,...,Errnn]Combine_kbErr=[(k1,b1,Err11),(k1,b2,Err12),(k1,b3,Err13)...,(k1,bn,Err1n)][(k2,b1,Err21),(k2,b2,Err22),(k2,b3,Err23)...,(k2,bn,Err2n)][(k3,b1,Err31),(k3,b2,Err32),(k3,b3,Err33)...,(k3,bn,Err3n)]..... 阅604 转1 评0 公众公开 17-05-18 09:24 |
我们要写一个函数，输入为X_parameters、Y_parameter和你要预测的平方英尺值，返回a、b和预测出的价格值。LinearRegression() regr.fit(X_parameters, Y_parameters) plt.scatter(X_parameters,Y_parameters,color=''blue'') plt.plot(X_parameters,regr.predict(X_parameters),color=''red'',linewidth... 阅1032 转5 评0 公众公开 17-05-18 08:58 |
<pre name="code" class="python">#计算Sales预测的RMSE print type(y_pred),type(y_test) print len(y_pred),len(y_test) print y_pred.shape,y_test.shape from sklearn import metrics import numpy as np sum_mean=0 for i in range(len(y_pred)): sum_mean+=(y_pred[i]-y_test.values[i])**2 sum_... 阅1614 转7 评0 公众公开 17-05-18 08:52 |
