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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 = [x...

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=3if t==1:    X = numpy.array(xList)         #Unnormalized X's    # X = numpy.array(xNormalized) ...

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ML之回归预测之Lasso：利用Lasso算法解决回归(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

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) &nb...

ML之回归预测之BE：利用BE算法解决回归(实数值评分预测)问题—线性方法解决非线性问题

输出结果设计思路代码实现for row in xList: newRow = list(row) alch = row[alchCol - 1] newRow.append((alch - 7) * (alch - 7)/...

TF之NN之回归预测：利用NN算法(RelU)实现根据三个自变量预测一个因变量的回归问题

实验数据设计思路输出结果loss is: 913.6623loss is: 781206160000.0loss is: 9006693000.0loss is: 103840136.0loss is: 1197209.2loss is: 13816.644loss is: 173.0564loss...

ML之回归预测：机器学习中的各种Regression回归算法、关键步骤配图

机器学习中的各种回归算法1、回归算法代码2、各种回归算法(1)、线性回归：紫色；Logistic回归：绿色(2)、局部加权回归3、各种回归算法大PK

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