Построить наивную байесовскую кривую, используя пакеты klaR и ROCR

Question

Здесь у меня есть задача классификации, и мне нужно использовать пакеты klaR и ROCR. Проблема в том, что кривая ROC имеет очень низкую производительность.

Вот мой код:

    #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)

Кривую РПЦ я построил:

Набор данных выглядит так (здесь только обучающий набор данных):

    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 < 0,5 предполагает, что ваши прогнозы хуже случайных - в основном вы предсказываете обратный класс. Это вызвано этой строкой:

score = nbprediction$posterior[, 1]

что дает вам вероятность того, что наблюдение принадлежит классу 0. Этот показатель будет высоким для класса 0, и ROCR ожидает, что высокие значения будут представлять класс 1. Вместо этого используйте второй столбец задней матрицы:

score = nbprediction$posterior[, 2]