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—在完整数据集上训练Lasso模型

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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—在完整数据集上训练Lasso模型

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)