ML之迴歸預測之Lasso:利用Lasso演算法解決迴歸(實數值評分預測)問題—優化模型【增加新(組合)屬性】
阿新 • • 發佈:2019-01-04
ML之迴歸預測之Lasso:利用Lasso演算法解決迴歸(實數值評分預測)問題—優化模型【增加新(組合)屬性】
輸出結果
設計思路
核心程式碼
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)/nrows xMeans.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