ML之分類預測之ElasticNet：利用ElasticNet回歸對二分類數據集構建二分類器(DIY交叉驗證+分類的兩種度量PK)

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核心代碼

#(4)交叉驗證 for ixval in range(nxval):idxTest = [a for a in range(nrow) if a%nxval == ixval%nxval]idxTrain = [a for a in range(nrow) if a%nxval != ixval%nxval]xTrain = numpy.array([xNormalized[r] for r in idxTrain])xTest = numpy.array([xNormalized[r] for r in idxTest])labelTrain = numpy.array([labelNormalized[r] for r in idxTrain])labelTest = numpy.array([labelNormalized[r] for r in idxTest])alphas, coefs, _ = enet_path(xTrain, labelTrain,l1_ratio=0.8, fit_intercept=False, return_models=False)if ixval == 0:pred = numpy.dot(xTest, coefs)yOut = labelTestelse:#accumulate predictions累積預測yTemp = numpy.array(yOut)yOut = numpy.concatenate((yTemp, labelTest), axis=0)#accumulate predictionspredTemp = numpy.array(pred)pred = numpy.concatenate((predTemp, numpy.dot(xTest, coefs)), axis = 0)#三處采樣P = len(idxPos) #P = Positive cases N = nrow - P #N = Negative cases#第52處采樣 TP = tpr[52] * P #TP = True positives = tpr * P FN = P - TP #FN = False negatives = P - TP FP = fpr[52] * N #FP = False positives = fpr * N TN = N - FP #TN = True negatives = N - FP print('52：Threshold Value = ', thresh[52]) print('TP = ', TP, 'FP = ', FP) print('FN = ', FN, 'TN = ', TN)#第104處采樣 TP = tpr[104] * P FN = P - TP FP = fpr[104] * N TN = N - FP print('104：Threshold Value = ', thresh[104]) print('TP = ', TP, 'FP = ', FP) print('FN = ', FN, 'TN = ', TN)#第156處采樣 TP = tpr[156] * P FN = P - TP FP = fpr[156] * N TN = N - FP print('156：Threshold Value = ', thresh[156]) print('TP = ', TP, 'FP = ', FP) print('FN = ', FN, 'TN = ', TN)

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