ML之分类预测之LARS：利用回归工具将二分类转为回归问题并采用LARS算法构建分类器

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输出结果

['V10', 'V48', 'V44', 'V11', 'V35', 'V51', 'V20', 'V3', 'V21', 'V15', 'V43', 'V0', 'V22', 'V45', 'V53', 'V27', 'V30', 'V50', 'V58', 'V46', 'V56', 'V28', 'V39']

设计思路

代码实现

for i in range(nSteps):
    residuals = [0.0] * nrow
    for j in range(nrow):
        labelsHat = sum([xNormalized[j][k] * beta[k] for k in range(ncol)])
        residuals[j] = labelNormalized[j] - labelsHat

    corr = [0.0] * ncol

    for j in range(ncol):
        corr[j] = sum([xNormalized[k][j] * residuals[k] for k in range(nrow)]) / nrow

    iStar = 0
    corrStar = corr[0]

    for j in range(1, (ncol)):
        if abs(corrStar) < abs(corr[j]):
            iStar = j; corrStar = corr[j]

    beta[iStar] += stepSize * corrStar / abs(corrStar)
    betaMat.append(list(beta))