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)

输出结果

设计思路

核心代码

t=3

if t==1:

   X = numpy.array(xList)         #Unnormalized X's

   # X = numpy.array(xNormalized)   #Normlized Xss

   Y = numpy.array(labels)          #Unnormalized labels

   # Y = numpy.array(labelNormalized) #normalized lables

elif t==2:

   X = numpy.array(xList)           #Unnormalized X's

   X = numpy.array(xNormalized)     #Normlized Xss

   Y = numpy.array(labels)          #Unnormalized labels

   Y = numpy.array(labelNormalized) #normalized lables

elif t==3:

   X = numpy.array(xList)           #Unnormalized X's

   X = numpy.array(xNormalized)     #Normlized Xss

   Y = numpy.array(labels)          #Unnormalized labels

   # Y = numpy.array(labelNormalized) #normalized lables

linear_model.lasso_path(X, Y,  return_models=False)