ML之回归预测之Lasso：利用Lasso算法对对红酒品质wine数据集解决回归(实数值评分预测)

ML之回归预测之Lasso：利用Lasso算法对对红酒品质wine数据集解决回归(实数值评分预测)问题—采用10折交叉验证(测试集error)来评估LassoCV模型

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

if 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 lables

ML之回归预测之Lasso：利用Lasso算法对对红酒品质wine数据集解决回归(实数值评分预测)问题—采用10折交叉验证(测试集error)来评估LassoCV模型