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
目录
输出结果
设计思路
代码实现
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] * nrowfor j in range(nrow):labelsHat = sum([xNormalized[j][k] * beta[k] for k in range(ncol)])residuals[j] = labelNormalized[j] - labelsHatcorr = [0.0] * ncolfor j in range(ncol):corr[j] = sum([xNormalized[k][j] * residuals[k] for k in range(nrow)]) / nrowiStar = 0corrStar = 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))
