Plot a Naive Bayes Curve Using klaR and ROCR package

Here I have a classification task and I need to use klaR and ROCR packages. The probelm is that the ROC Curve has a very low performance.

Here is my code:

    #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)

The ROC Curve I plotted:

The dataset looks like (only training dataset here):

    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

Solution

An AUC < 0.5 suggests that your predictions are worse than random - basically you are predicting the reverse class. This is caused by this line:

score = nbprediction$posterior[, 1]

which gives you the probability that an observation belongs to class 0. This score will be high for class 0, and ROCR expects high values to represent class 1. Use the second column of the posterior matrix instead:

score = nbprediction$posterior[, 2]