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—在完整資料集上訓練Lasso模型

輸出結果

設計思路

核心程式碼

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)