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—优化模型【增加新(组合)属性】

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—优化模型【增加新(组合)属性】

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—优化模型【增加新(组合)属性】

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

names[-1] = "a^2"
names.append("a*b")nrows = len(xList)
ncols = len(xList[0])xMeans = []
xSD = []
for i in range(ncols):col = [xList[j][i] for j in range(nrows)]mean = sum(col)/nrowsxMeans.append(mean)colDiff = [(xList[j][i] - mean) for j in range(nrows)]sumSq = sum([colDiff[i] * colDiff[i] for i in range(nrows)])stdDev = sqrt(sumSq/nrows)xSD.append(stdDev)X = numpy.array(xList)             #Unnormalized X's
X = numpy.array(xNormalized)       #Normlized Xss
Y = numpy.array(labels)            #Unnormalized labels