ML之PLiR之LARS：利用LARS算法求解ElasticNet回归类型问题(实数值评分预测)

letme 2022-09-19 16:03:14  52513

分类专栏：资讯

设计思路

输出结果

1、LARS

2、10-fold cross validation

实现代码

设计思路

更新……

输出结果

['"alcohol"', '"volatile acidity"', '"sulphates"', '"total sulfur dioxide"', '"chlorides"', '"fixed acidity"', '"pH"', '"free sulfur dioxide"', '"citric acid"', '"residual sugar"', '"density"']

1、LARS

2、10-fold cross validation

Minimum Mean Square Error 0.5873018933136459
Index of Minimum Mean Square Error 311

实现代码


initialize a vector of coefficients beta
beta = [0.0] * ncols
 
initialize matrix of betas at each step
betaMat = []
betaMat.append(list(beta))
 
 
number of steps to take
nSteps = 350
stepSize = 0.004
nzList = []
 
for i in range(nSteps):
    calculate residuals
    residuals = [0.0] * nrows
    for j in range(nrows):
        labelsHat = sum([xNormalized[j][k] * beta[k] for k in range(ncols)])
        residuals[j] = labelNormalized[j] - labelsHat
 
    calculate correlation between attribute columns from normalized wine and residual
    corr = [0.0] * ncols
 
    for j in range(ncols):
        corr[j] = sum([xNormalized[k][j] * residuals[k] for k in range(nrows)]) / nrows
 
    iStar = 0
    corrStar = corr[0]
 
    for j in range(1, (ncols)):
        if abs(corrStar) < abs(corr[j]):
            iStar = j; corrStar = corr[j]
 
    beta[iStar] += stepSize * corrStar / abs(corrStar)
    betaMat.append(list(beta))
 
 
    nzBeta = [index for index in range(ncols) if beta[index] != 0.0]
    for q in nzBeta:
        if (q in nzList) == False:
            nzList.append(q)
 
nameList = [names[nzList[i]] for i in range(len(nzList))]
 
print(nameList)
for i in range(ncols):
    plot range of beta values for each attribute
    coefCurve = [betaMat[k][i] for k in range(nSteps)]
    xaxis = range(nSteps)
    plot.plot(xaxis, coefCurve)

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ML之PLiR之LARS：利用LARS算法求解ElasticNet回归类型问题(实数值评分预测)

设计思路

输出结果

1、LARS

2、10-fold cross validation

实现代码

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