ML之回归预测之Lasso：利用Lasso算法对红酒品质wine数据集实现红酒口感评分预测(实数值评分预测)
目录
输出结果
设计思路
核心代码
输出结果
设计思路
核心代码
t=3if t==1:X = numpy.array(xList) #Unnormalized X's# X = numpy.array(xNormalized) #Normlized XssY = numpy.array(labels)#Unnormalized labels# Y = numpy.array(labelNormalized) #normalized lableselif t==2:X = numpy.array(xList) #Unnormalized X'sX = numpy.array(xNormalized)#Normlized XssY = numpy.array(labels)#Unnormalized labelsY = numpy.array(labelNormalized) #normalized lableselif t==3:X = numpy.array(xList) #Unnormalized X'sX = numpy.array(xNormalized)#Normlized XssY = numpy.array(labels)#Unnormalized labels# Y = numpy.array(labelNormalized) #normalized lableslinear_model.lasso_path(X, Y, return_models=False)
ML之回归预测之Lasso：利用Lasso算法对红酒品质wine数据集实现红酒口感评分预测(实数值评分预测)
