Question

这里我有一个分类任务，我需要使用klaR和ROCR包。问题是ROC曲线的性能非常低。

这是我的代码：

    #install the klaR package for naiveBayes
    install.packages('klaR')
    library(klaR)
    library(ROCR)

    #create data set 
    train<-read.table(file="train.txt",header=TRUE,sep=",")
    test<- read.table(file="test.txt",header=TRUE,sep=",")
    train$Type <- as.factor((train$Type))
    test$Type <- as.factor((test$Type))

    #build nb model and make predictions 
    nb <- NaiveBayes(Type ~ ., data = train)
    nbprediction = predict(nb, test, type='raw')

    #Plot Roc Curve
    score = nbprediction$posterior[, 1]
    actual.class = test$Type
    pred = prediction(score, actual.class)
    nb.prff = performance(pred, "tpr", "fpr")
    plot(nb.prff)

我绘制的ROC曲线：

数据集看起来像（这里只有训练数据集）：

    Area,Perimeter,Compactness,Length,Width,Asymmetry,Groove,Type
    14.8,14.52,0.8823,5.656,3.288,3.112,5.309,1
    14.79,14.52,0.8819,5.545,3.291,2.704,5.111,1
    14.99,14.56,0.8883,5.57,3.377,2.958,5.175,1
    19.14,16.61,0.8722,6.259,3.737,6.682,6.053,0
    15.69,14.75,0.9058,5.527,3.514,1.599,5.046,1
    14.11,14.26,0.8722,5.52,3.168,2.688,5.219,1
    13.16,13.55,0.9009,5.138,3.201,2.461,4.783,1
    16.16,15.33,0.8644,5.845,3.395,4.266,5.795,0
    15.01,14.76,0.8657,5.789,3.245,1.791,5.001,1
    14.11,14.1,0.8911,5.42,3.302,2.7,5,1
    17.98,15.85,0.8993,5.979,3.687,2.257,5.919,0
    21.18,17.21,0.8989,6.573,4.033,5.78,6.231,0
    14.29,14.09,0.905,5.291,3.337,2.699,4.825,1
    14.59,14.28,0.8993,5.351,3.333,4.185,4.781,1
    11.42,12.86,0.8683,5.008,2.85,2.7,4.607,1
    12.11,13.47,0.8392,5.159,3.032,1.502,4.519,1
    15.6,15.11,0.858,5.832,3.286,2.725,5.752,0
    15.38,14.66,0.899,5.477,3.465,3.6,5.439,0
    18.94,16.49,0.875,6.445,3.639,5.064,6.362,0
    12.36,13.19,0.8923,5.076,3.042,3.22,4.605,1
    14.01,14.29,0.8625,5.609,3.158,2.217,5.132,1
    17.12,15.55,0.8892,5.85,3.566,2.858,5.746,0
    15.78,14.91,0.8923,5.674,3.434,5.593,5.136,1
    16.19,15.16,0.8849,5.833,3.421,0.903,5.307,1
    14.43,14.4,0.8751,5.585,3.272,3.975,5.144,1
    13.8,14.04,0.8794,5.376,3.155,1.56,4.961,1
    14.46,14.35,0.8818,5.388,3.377,2.802,5.044,1
    18.59,16.05,0.9066,6.037,3.86,6.001,5.877,0
    18.75,16.18,0.8999,6.111,3.869,4.188,5.992,0
    15.49,14.94,0.8724,5.757,3.371,3.412,5.228,1
    12.73,13.75,0.8458,5.412,2.882,3.533,5.067,1
    13.5,13.85,0.8852,5.351,3.158,2.249,5.176,1
    14.38,14.21,0.8951,5.386,3.312,2.462,4.956,1
    14.86,14.67,0.8676,5.678,3.258,2.129,5.351,1
    18.45,16.12,0.8921,6.107,3.769,2.235,5.794,0
    17.32,15.91,0.8599,6.064,3.403,3.824,5.922,0
    20.2,16.89,0.8894,6.285,3.864,5.173,6.187,0
    20.03,16.9,0.8811,6.493,3.857,3.063,6.32,0
    18.14,16.12,0.8772,6.059,3.563,3.619,6.011,0
    13.99,13.83,0.9183,5.119,3.383,5.234,4.781,1
    15.57,15.15,0.8527,5.92,3.231,2.64,5.879,0
    16.2,15.27,0.8734,5.826,3.464,2.823,5.527,1
    20.97,17.25,0.8859,6.563,3.991,4.677,6.316,0
    14.16,14.4,0.8584,5.658,3.129,3.072,5.176,1
    13.45,14.02,0.8604,5.516,3.065,3.531,5.097,1
    15.5,14.86,0.882,5.877,3.396,4.711,5.528,1
    16.77,15.62,0.8638,5.927,3.438,4.92,5.795,0
    12.74,13.67,0.8564,5.395,2.956,2.504,4.869,1
    14.88,14.57,0.8811,5.554,3.333,1.018,4.956,1
    14.28,14.17,0.8944,5.397,3.298,6.685,5.001,1
    14.34,14.37,0.8726,5.63,3.19,1.313,5.15,1
    14.03,14.16,0.8796,5.438,3.201,1.717,5.001,1
    19.11,16.26,0.9081,6.154,3.93,2.936,6.079,0
    14.52,14.6,0.8557,5.741,3.113,1.481,5.487,1
    18.43,15.97,0.9077,5.98,3.771,2.984,5.905,0
    18.81,16.29,0.8906,6.272,3.693,3.237,6.053,0
    13.78,14.06,0.8759,5.479,3.156,3.136,4.872,1
    14.69,14.49,0.8799,5.563,3.259,3.586,5.219,1
    18.85,16.17,0.9056,6.152,3.806,2.843,6.2,0
    12.88,13.5,0.8879,5.139,3.119,2.352,4.607,1
    12.78,13.57,0.8716,5.262,3.026,1.176,4.782,1
    14.33,14.28,0.8831,5.504,3.199,3.328,5.224,1
    19.46,16.5,0.8985,6.113,3.892,4.308,6.009,0
    19.38,16.72,0.8716,6.303,3.791,3.678,5.965,0
    15.26,14.85,0.8696,5.714,3.242,4.543,5.314,1
    20.24,16.91,0.8897,6.315,3.962,5.901,6.188,0
    19.94,16.92,0.8752,6.675,3.763,3.252,6.55,0
    20.71,17.23,0.8763,6.579,3.814,4.451,6.451,0
    16.17,15.38,0.8588,5.762,3.387,4.286,5.703,0
    13.02,13.76,0.8641,5.395,3.026,3.373,4.825,1
    16.53,15.34,0.8823,5.875,3.467,5.532,5.88,0
    13.89,14.02,0.888,5.439,3.199,3.986,4.738,1
    18.98,16.57,0.8687,6.449,3.552,2.144,6.453,0
    17.08,15.38,0.9079,5.832,3.683,2.956,5.484,1
    15.03,14.77,0.8658,5.702,3.212,1.933,5.439,1
    16.14,14.99,0.9034,5.658,3.562,1.355,5.175,1
    18.65,16.41,0.8698,6.285,3.594,4.391,6.102,0
    20.1,16.99,0.8746,6.581,3.785,1.955,6.449,0
    17.99,15.86,0.8992,5.89,3.694,2.068,5.837,0
    15.88,14.9,0.8988,5.618,3.507,0.7651,5.091,1
    13.22,13.84,0.868,5.395,3.07,4.157,5.088,1
    18.3,15.89,0.9108,5.979,3.755,2.837,5.962,0
    19.51,16.71,0.878,6.366,3.801,2.962,6.185,0

Answer 1

AUC＆lt; 0.5表明你的预测比随机更糟糕 - 基本上你是在预测反向阶级。这是由这一行引起的：

score = nbprediction$posterior[, 1]

，它为您提供观察属于0级的概率。对于0级，此分数将为高，ROCR期望高值代表1级。使用后验矩阵的第2列代替：< / p>

score = nbprediction$posterior[, 2]

使用klaR和ROCR包绘制朴素贝叶斯曲线

1 个答案: