Unified (formula-based) interface version of the naive Bayes algorithm
provided by e1071::naiveBayes().
Usage
mlNaiveBayes(train, ...)
ml_naive_bayes(train, ...)
# S3 method for formula
mlNaiveBayes(formula, data, laplace = 0, ..., subset, na.action)
# S3 method for default
mlNaiveBayes(train, response, laplace = 0, ...)
# S3 method for mlNaiveBayes
predict(
object,
newdata,
type = c("class", "membership", "both"),
method = c("direct", "cv"),
na.action = na.exclude,
threshold = 0.001,
eps = 0,
...
)Arguments
- train
a matrix or data frame with predictors.
- ...
further arguments passed to the classification method or its
predict()method (not used here for now).- formula
a formula with left term being the factor variable to predict and the right term with the list of independent, predictive variables, separated with a plus sign. If the data frame provided contains only the dependent and independent variables, one can use the
class ~ .short version (that one is strongly encouraged). Variables with minus sign are eliminated. Calculations on variables are possible according to usual formula convention (possibly protected by usingI()).- data
a data.frame to use as a training set.
- laplace
positive number controlling Laplace smoothing for the naive Bayes classifier. The default (0) disables Laplace smoothing.
- subset
index vector with the cases to define the training set in use (this argument must be named, if provided).
- na.action
function to specify the action to be taken if
NAs are found. Forml_naive_bayes()na.failis used by default. The calculation is stopped if there is anyNAin the data. Another option isna.omit, where cases with missing values on any required variable are dropped (this argument must be named, if provided). For thepredict()method, the default, and most suitable option, isna.exclude. In that case, rows withNAs innewdata=are excluded from prediction, but reinjected in the final results so that the number of items is still the same (and in the same order asnewdata=).- response
a vector of factor with the classes.
- object
an mlNaiveBayes object
- newdata
a new dataset with same conformation as the training set (same variables, except may by the class for classification or dependent variable for regression). Usually a test set, or a new dataset to be predicted.
- type
the type of prediction to return.
"class"by default, the predicted classes. Other options are"membership", the posterior probability or"both"to return classes and memberships,- method
"direct"(default) or"cv"."direct"predicts new cases innewdata=if this argument is provided, or the cases in the training set if not. Take care that not providingnewdata=means that you just calculate the self-consistency of the classifier but cannot use the metrics derived from these results for the assessment of its performances. Either use a different dataset innewdata=or use the alternate cross-validation ("cv") technique. If you specifymethod = "cv"thencvpredict()is used and you cannot providenewdata=in that case.- threshold
value replacing cells with probabilities within 'eps' range.
- eps
number for specifying an epsilon-range to apply Laplace smoothing (to replace zero or close-zero probabilities by 'threshold').
Value
ml_naive_bayes()/mlNaiveBayes() creates an mlNaiveBayes,
mlearning object containing the classifier and a lot of additional
metadata used by the functions and methods you can apply to it like
predict() or cvpredict(). In case you want to program new functions or
extract specific components, inspect the "unclassed" object using
unclass().
See also
mlearning(), cvpredict(), confusion(), also
e1071::naiveBayes() that actually does the classification.
Examples
# Prepare data: split into training set (2/3) and test set (1/3)
data("iris", package = "datasets")
train <- c(1:34, 51:83, 101:133)
iris_train <- iris[train, ]
iris_test <- iris[-train, ]
# One case with missing data in train set, and another case in test set
iris_train[1, 1] <- NA
iris_test[25, 2] <- NA
iris_nb <- ml_naive_bayes(data = iris_train, Species ~ .)
summary(iris_nb)
#> A mlearning object of class mlNaiveBayes (naive Bayes classifier):
#> Initial call: mlNaiveBayes.formula(formula = Species ~ ., data = iris_train)
#>
#> Naive Bayes Classifier for Discrete Predictors
#>
#> Call:
#> naiveBayes.default(x = train, y = response, laplace = laplace,
#> .args. = ..1)
#>
#> A-priori probabilities:
#> response
#> setosa versicolor virginica
#> 0.3333333 0.3333333 0.3333333
#>
#> Conditional probabilities:
#> Sepal.Length
#> response [,1] [,2]
#> setosa 5.048485 0.3725933
#> versicolor 6.027273 0.5392545
#> virginica 6.642424 0.7088857
#>
#> Sepal.Width
#> response [,1] [,2]
#> setosa 3.478788 0.3805897
#> versicolor 2.763636 0.3267436
#> virginica 2.951515 0.3545430
#>
#> Petal.Length
#> response [,1] [,2]
#> setosa 1.478788 0.1781109
#> versicolor 4.284848 0.4651083
#> virginica 5.642424 0.6179757
#>
#> Petal.Width
#> response [,1] [,2]
#> setosa 0.2454545 0.1033529
#> versicolor 1.3303030 0.2157615
#> virginica 2.0090909 0.2466825
#>
predict(iris_nb) # Default type is class
#> [1] setosa setosa setosa setosa setosa setosa
#> [7] setosa setosa setosa setosa setosa setosa
#> [13] setosa setosa setosa setosa setosa setosa
#> [19] setosa setosa setosa setosa setosa setosa
#> [25] setosa setosa setosa setosa setosa setosa
#> [31] setosa setosa setosa versicolor versicolor versicolor
#> [37] versicolor versicolor versicolor versicolor versicolor versicolor
#> [43] versicolor versicolor versicolor versicolor versicolor versicolor
#> [49] versicolor versicolor versicolor versicolor versicolor virginica
#> [55] versicolor versicolor versicolor versicolor versicolor versicolor
#> [61] virginica versicolor versicolor versicolor versicolor versicolor
#> [67] virginica virginica virginica virginica virginica virginica
#> [73] versicolor virginica virginica virginica virginica virginica
#> [79] virginica virginica virginica virginica virginica virginica
#> [85] virginica versicolor virginica virginica virginica virginica
#> [91] virginica virginica virginica virginica virginica virginica
#> [97] virginica virginica virginica
#> Levels: setosa versicolor virginica
predict(iris_nb, type = "membership")
#> setosa versicolor virginica
#> [1,] 1.000000e+00 1.803036e-16 1.167697e-24
#> [2,] 1.000000e+00 1.352715e-17 1.831338e-25
#> [3,] 1.000000e+00 1.772241e-16 1.353873e-24
#> [4,] 1.000000e+00 5.692804e-18 1.376409e-25
#> [5,] 1.000000e+00 6.093062e-14 6.531376e-21
#> [6,] 1.000000e+00 8.826228e-17 2.837739e-24
#> [7,] 1.000000e+00 7.437194e-17 8.757620e-25
#> [8,] 1.000000e+00 1.216747e-16 9.720500e-25
#> [9,] 1.000000e+00 6.724341e-17 2.531533e-25
#> [10,] 1.000000e+00 4.566759e-17 1.186716e-24
#> [11,] 1.000000e+00 1.879913e-16 1.981529e-24
#> [12,] 1.000000e+00 2.686953e-17 1.085926e-25
#> [13,] 1.000000e+00 1.650638e-18 2.251328e-26
#> [14,] 1.000000e+00 2.368402e-18 3.908159e-25
#> [15,] 1.000000e+00 1.761543e-16 1.792568e-22
#> [16,] 1.000000e+00 2.706070e-16 6.524103e-23
#> [17,] 1.000000e+00 1.684941e-16 5.447395e-24
#> [18,] 1.000000e+00 2.886607e-14 1.441825e-21
#> [19,] 1.000000e+00 6.739186e-17 4.013941e-24
#> [20,] 1.000000e+00 9.227945e-15 7.619346e-23
#> [21,] 1.000000e+00 3.076912e-15 2.554200e-22
#> [22,] 1.000000e+00 2.412671e-19 2.082673e-26
#> [23,] 1.000000e+00 5.291974e-11 2.164940e-18
#> [24,] 1.000000e+00 8.216570e-14 5.480528e-22
#> [25,] 1.000000e+00 3.650319e-15 1.524151e-23
#> [26,] 1.000000e+00 7.652882e-14 2.445210e-21
#> [27,] 1.000000e+00 7.812216e-17 1.149116e-24
#> [28,] 1.000000e+00 4.476095e-17 6.365960e-25
#> [29,] 1.000000e+00 5.252216e-16 3.756987e-24
#> [30,] 1.000000e+00 1.184327e-15 6.488869e-24
#> [31,] 1.000000e+00 8.338509e-14 3.263362e-21
#> [32,] 1.000000e+00 1.690882e-19 8.721554e-27
#> [33,] 1.000000e+00 4.316419e-19 7.033715e-26
#> [34,] 7.689655e-103 9.159085e-01 8.409150e-02
#> [35,] 1.096673e-96 9.667123e-01 3.328772e-02
#> [36,] 1.930514e-116 6.802159e-01 3.197841e-01
#> [37,] 1.102081e-67 9.999412e-01 5.879818e-05
#> [38,] 3.602267e-102 9.709066e-01 2.909345e-02
#> [39,] 3.335613e-86 9.993121e-01 6.878536e-04
#> [40,] 1.129911e-109 7.783585e-01 2.216415e-01
#> [41,] 2.982269e-33 9.999994e-01 5.938056e-07
#> [42,] 4.546204e-93 9.957187e-01 4.281285e-03
#> [43,] 2.245740e-67 9.998022e-01 1.977854e-04
#> [44,] 1.295311e-39 9.999994e-01 6.198654e-07
#> [45,] 8.639091e-84 9.962009e-01 3.799095e-03
#> [46,] 1.306052e-57 9.999962e-01 3.800063e-06
#> [47,] 5.129110e-100 9.913456e-01 8.654381e-03
#> [48,] 1.247004e-53 9.999529e-01 4.710380e-05
#> [49,] 2.751640e-89 9.894678e-01 1.053225e-02
#> [50,] 9.204587e-95 9.910781e-01 8.921933e-03
#> [51,] 1.056753e-59 9.999929e-01 7.109959e-06
#> [52,] 4.759450e-98 9.939246e-01 6.075414e-03
#> [53,] 4.172627e-56 9.999931e-01 6.930611e-06
#> [54,] 1.147499e-124 2.598343e-01 7.401657e-01
#> [55,] 2.430851e-68 9.998174e-01 1.826315e-04
#> [56,] 3.305367e-115 9.485689e-01 5.143106e-02
#> [57,] 1.322349e-91 9.991780e-01 8.219604e-04
#> [58,] 3.383621e-80 9.990860e-01 9.139928e-04
#> [59,] 4.018503e-89 9.929331e-01 7.066888e-03
#> [60,] 2.885304e-107 9.596243e-01 4.037565e-02
#> [61,] 1.500417e-131 1.709929e-01 8.290071e-01
#> [62,] 4.494759e-96 9.893143e-01 1.068568e-02
#> [63,] 5.408043e-40 9.999988e-01 1.216458e-06
#> [64,] 1.002758e-52 9.999955e-01 4.522882e-06
#> [65,] 5.628947e-46 9.999987e-01 1.332260e-06
#> [66,] 4.602589e-60 9.999711e-01 2.887912e-05
#> [67,] 4.079871e-244 3.576071e-09 1.000000e+00
#> [68,] 3.689196e-146 5.139596e-02 9.486040e-01
#> [69,] 4.834773e-210 1.388701e-06 9.999986e-01
#> [70,] 1.096501e-167 4.868244e-03 9.951318e-01
#> [71,] 1.614505e-208 2.397766e-06 9.999976e-01
#> [72,] 1.416791e-258 1.522784e-09 1.000000e+00
#> [73,] 4.215784e-105 9.531740e-01 4.682597e-02
#> [74,] 1.247937e-215 3.770575e-06 9.999962e-01
#> [75,] 1.109378e-181 1.112506e-03 9.988875e-01
#> [76,] 1.716419e-254 1.088139e-10 1.000000e+00
#> [77,] 4.973153e-155 2.017456e-03 9.979825e-01
#> [78,] 1.101493e-158 8.236383e-03 9.917636e-01
#> [79,] 3.008552e-185 3.859199e-05 9.999614e-01
#> [80,] 3.744500e-148 2.931546e-02 9.706845e-01
#> [81,] 3.347013e-184 2.234062e-05 9.999777e-01
#> [82,] 8.606627e-188 8.413290e-06 9.999916e-01
#> [83,] 7.840619e-163 5.273864e-03 9.947261e-01
#> [84,] 1.759992e-272 1.484798e-11 1.000000e+00
#> [85,] 8.472771e-297 6.900804e-12 1.000000e+00
#> [86,] 2.360925e-119 9.577011e-01 4.229886e-02
#> [87,] 2.229069e-212 2.646252e-07 9.999997e-01
#> [88,] 1.285230e-142 3.410444e-02 9.658956e-01
#> [89,] 2.856722e-259 2.856759e-09 1.000000e+00
#> [90,] 1.186200e-131 2.371171e-01 7.628829e-01
#> [91,] 1.619220e-195 7.614926e-06 9.999924e-01
#> [92,] 1.704706e-195 2.566160e-05 9.999743e-01
#> [93,] 1.903352e-126 3.334420e-01 6.665580e-01
#> [94,] 2.236975e-130 2.044628e-01 7.955372e-01
#> [95,] 3.459428e-189 6.688505e-05 9.999331e-01
#> [96,] 1.646362e-171 1.997026e-03 9.980030e-01
#> [97,] 2.796148e-210 3.580998e-06 9.999964e-01
#> [98,] 1.557094e-238 1.596656e-09 1.000000e+00
#> [99,] 7.874332e-197 1.449503e-05 9.999855e-01
predict(iris_nb, type = "both")
#> $class
#> [1] setosa setosa setosa setosa setosa setosa
#> [7] setosa setosa setosa setosa setosa setosa
#> [13] setosa setosa setosa setosa setosa setosa
#> [19] setosa setosa setosa setosa setosa setosa
#> [25] setosa setosa setosa setosa setosa setosa
#> [31] setosa setosa setosa versicolor versicolor versicolor
#> [37] versicolor versicolor versicolor versicolor versicolor versicolor
#> [43] versicolor versicolor versicolor versicolor versicolor versicolor
#> [49] versicolor versicolor versicolor versicolor versicolor virginica
#> [55] versicolor versicolor versicolor versicolor versicolor versicolor
#> [61] virginica versicolor versicolor versicolor versicolor versicolor
#> [67] virginica virginica virginica virginica virginica virginica
#> [73] versicolor virginica virginica virginica virginica virginica
#> [79] virginica virginica virginica virginica virginica virginica
#> [85] virginica versicolor virginica virginica virginica virginica
#> [91] virginica virginica virginica virginica virginica virginica
#> [97] virginica virginica virginica
#> Levels: setosa versicolor virginica
#>
#> $membership
#> setosa versicolor virginica
#> [1,] 1.000000e+00 1.803036e-16 1.167697e-24
#> [2,] 1.000000e+00 1.352715e-17 1.831338e-25
#> [3,] 1.000000e+00 1.772241e-16 1.353873e-24
#> [4,] 1.000000e+00 5.692804e-18 1.376409e-25
#> [5,] 1.000000e+00 6.093062e-14 6.531376e-21
#> [6,] 1.000000e+00 8.826228e-17 2.837739e-24
#> [7,] 1.000000e+00 7.437194e-17 8.757620e-25
#> [8,] 1.000000e+00 1.216747e-16 9.720500e-25
#> [9,] 1.000000e+00 6.724341e-17 2.531533e-25
#> [10,] 1.000000e+00 4.566759e-17 1.186716e-24
#> [11,] 1.000000e+00 1.879913e-16 1.981529e-24
#> [12,] 1.000000e+00 2.686953e-17 1.085926e-25
#> [13,] 1.000000e+00 1.650638e-18 2.251328e-26
#> [14,] 1.000000e+00 2.368402e-18 3.908159e-25
#> [15,] 1.000000e+00 1.761543e-16 1.792568e-22
#> [16,] 1.000000e+00 2.706070e-16 6.524103e-23
#> [17,] 1.000000e+00 1.684941e-16 5.447395e-24
#> [18,] 1.000000e+00 2.886607e-14 1.441825e-21
#> [19,] 1.000000e+00 6.739186e-17 4.013941e-24
#> [20,] 1.000000e+00 9.227945e-15 7.619346e-23
#> [21,] 1.000000e+00 3.076912e-15 2.554200e-22
#> [22,] 1.000000e+00 2.412671e-19 2.082673e-26
#> [23,] 1.000000e+00 5.291974e-11 2.164940e-18
#> [24,] 1.000000e+00 8.216570e-14 5.480528e-22
#> [25,] 1.000000e+00 3.650319e-15 1.524151e-23
#> [26,] 1.000000e+00 7.652882e-14 2.445210e-21
#> [27,] 1.000000e+00 7.812216e-17 1.149116e-24
#> [28,] 1.000000e+00 4.476095e-17 6.365960e-25
#> [29,] 1.000000e+00 5.252216e-16 3.756987e-24
#> [30,] 1.000000e+00 1.184327e-15 6.488869e-24
#> [31,] 1.000000e+00 8.338509e-14 3.263362e-21
#> [32,] 1.000000e+00 1.690882e-19 8.721554e-27
#> [33,] 1.000000e+00 4.316419e-19 7.033715e-26
#> [34,] 7.689655e-103 9.159085e-01 8.409150e-02
#> [35,] 1.096673e-96 9.667123e-01 3.328772e-02
#> [36,] 1.930514e-116 6.802159e-01 3.197841e-01
#> [37,] 1.102081e-67 9.999412e-01 5.879818e-05
#> [38,] 3.602267e-102 9.709066e-01 2.909345e-02
#> [39,] 3.335613e-86 9.993121e-01 6.878536e-04
#> [40,] 1.129911e-109 7.783585e-01 2.216415e-01
#> [41,] 2.982269e-33 9.999994e-01 5.938056e-07
#> [42,] 4.546204e-93 9.957187e-01 4.281285e-03
#> [43,] 2.245740e-67 9.998022e-01 1.977854e-04
#> [44,] 1.295311e-39 9.999994e-01 6.198654e-07
#> [45,] 8.639091e-84 9.962009e-01 3.799095e-03
#> [46,] 1.306052e-57 9.999962e-01 3.800063e-06
#> [47,] 5.129110e-100 9.913456e-01 8.654381e-03
#> [48,] 1.247004e-53 9.999529e-01 4.710380e-05
#> [49,] 2.751640e-89 9.894678e-01 1.053225e-02
#> [50,] 9.204587e-95 9.910781e-01 8.921933e-03
#> [51,] 1.056753e-59 9.999929e-01 7.109959e-06
#> [52,] 4.759450e-98 9.939246e-01 6.075414e-03
#> [53,] 4.172627e-56 9.999931e-01 6.930611e-06
#> [54,] 1.147499e-124 2.598343e-01 7.401657e-01
#> [55,] 2.430851e-68 9.998174e-01 1.826315e-04
#> [56,] 3.305367e-115 9.485689e-01 5.143106e-02
#> [57,] 1.322349e-91 9.991780e-01 8.219604e-04
#> [58,] 3.383621e-80 9.990860e-01 9.139928e-04
#> [59,] 4.018503e-89 9.929331e-01 7.066888e-03
#> [60,] 2.885304e-107 9.596243e-01 4.037565e-02
#> [61,] 1.500417e-131 1.709929e-01 8.290071e-01
#> [62,] 4.494759e-96 9.893143e-01 1.068568e-02
#> [63,] 5.408043e-40 9.999988e-01 1.216458e-06
#> [64,] 1.002758e-52 9.999955e-01 4.522882e-06
#> [65,] 5.628947e-46 9.999987e-01 1.332260e-06
#> [66,] 4.602589e-60 9.999711e-01 2.887912e-05
#> [67,] 4.079871e-244 3.576071e-09 1.000000e+00
#> [68,] 3.689196e-146 5.139596e-02 9.486040e-01
#> [69,] 4.834773e-210 1.388701e-06 9.999986e-01
#> [70,] 1.096501e-167 4.868244e-03 9.951318e-01
#> [71,] 1.614505e-208 2.397766e-06 9.999976e-01
#> [72,] 1.416791e-258 1.522784e-09 1.000000e+00
#> [73,] 4.215784e-105 9.531740e-01 4.682597e-02
#> [74,] 1.247937e-215 3.770575e-06 9.999962e-01
#> [75,] 1.109378e-181 1.112506e-03 9.988875e-01
#> [76,] 1.716419e-254 1.088139e-10 1.000000e+00
#> [77,] 4.973153e-155 2.017456e-03 9.979825e-01
#> [78,] 1.101493e-158 8.236383e-03 9.917636e-01
#> [79,] 3.008552e-185 3.859199e-05 9.999614e-01
#> [80,] 3.744500e-148 2.931546e-02 9.706845e-01
#> [81,] 3.347013e-184 2.234062e-05 9.999777e-01
#> [82,] 8.606627e-188 8.413290e-06 9.999916e-01
#> [83,] 7.840619e-163 5.273864e-03 9.947261e-01
#> [84,] 1.759992e-272 1.484798e-11 1.000000e+00
#> [85,] 8.472771e-297 6.900804e-12 1.000000e+00
#> [86,] 2.360925e-119 9.577011e-01 4.229886e-02
#> [87,] 2.229069e-212 2.646252e-07 9.999997e-01
#> [88,] 1.285230e-142 3.410444e-02 9.658956e-01
#> [89,] 2.856722e-259 2.856759e-09 1.000000e+00
#> [90,] 1.186200e-131 2.371171e-01 7.628829e-01
#> [91,] 1.619220e-195 7.614926e-06 9.999924e-01
#> [92,] 1.704706e-195 2.566160e-05 9.999743e-01
#> [93,] 1.903352e-126 3.334420e-01 6.665580e-01
#> [94,] 2.236975e-130 2.044628e-01 7.955372e-01
#> [95,] 3.459428e-189 6.688505e-05 9.999331e-01
#> [96,] 1.646362e-171 1.997026e-03 9.980030e-01
#> [97,] 2.796148e-210 3.580998e-06 9.999964e-01
#> [98,] 1.557094e-238 1.596656e-09 1.000000e+00
#> [99,] 7.874332e-197 1.449503e-05 9.999855e-01
#>
# Self-consistency, do not use for assessing classifier performances!
confusion(iris_nb)
#> 99 items classified with 95 true positives (error rate = 4%)
#> Predicted
#> Actual 01 02 03 (sum) (FNR%)
#> 01 setosa 33 0 0 33 0
#> 02 versicolor 0 31 2 33 6
#> 03 virginica 0 2 31 33 6
#> (sum) 33 33 33 99 4
# Use an independent test set instead
confusion(predict(iris_nb, newdata = iris_test), iris_test$Species)
#> 50 items classified with 47 true positives (error rate = 6%)
#> Predicted
#> Actual 01 02 03 04 (sum) (FNR%)
#> 01 setosa 16 0 0 0 16 0
#> 02 NA 0 0 0 0 0
#> 03 versicolor 0 1 16 0 17 6
#> 04 virginica 0 0 2 15 17 12
#> (sum) 16 1 18 15 50 6
# Another dataset
data("HouseVotes84", package = "mlbench")
house_nb <- ml_naive_bayes(data = HouseVotes84, Class ~ .,
na.action = na.omit)
summary(house_nb)
#> A mlearning object of class mlNaiveBayes (naive Bayes classifier):
#> Initial call: mlNaiveBayes.formula(formula = Class ~ ., data = HouseVotes84, na.action = na.omit)
#>
#> Naive Bayes Classifier for Discrete Predictors
#>
#> Call:
#> naiveBayes.default(x = train, y = response, laplace = laplace,
#> .args. = ..1)
#>
#> A-priori probabilities:
#> response
#> democrat republican
#> 0.5344828 0.4655172
#>
#> Conditional probabilities:
#> V1
#> response n y
#> democrat 0.4112903 0.5887097
#> republican 0.7870370 0.2129630
#>
#> V2
#> response n y
#> democrat 0.5483871 0.4516129
#> republican 0.5277778 0.4722222
#>
#> V3
#> response n y
#> democrat 0.1451613 0.8548387
#> republican 0.8425926 0.1574074
#>
#> V4
#> response n y
#> democrat 0.951612903 0.048387097
#> republican 0.009259259 0.990740741
#>
#> V5
#> response n y
#> democrat 0.7983871 0.2016129
#> republican 0.0462963 0.9537037
#>
#> V6
#> response n y
#> democrat 0.5564516 0.4435484
#> republican 0.1296296 0.8703704
#>
#> V7
#> response n y
#> democrat 0.2338710 0.7661290
#> republican 0.7314815 0.2685185
#>
#> V8
#> response n y
#> democrat 0.1693548 0.8306452
#> republican 0.8518519 0.1481481
#>
#> V9
#> response n y
#> democrat 0.2096774 0.7903226
#> republican 0.8611111 0.1388889
#>
#> V10
#> response n y
#> democrat 0.4677419 0.5322581
#> republican 0.4259259 0.5740741
#>
#> V11
#> response n y
#> democrat 0.4919355 0.5080645
#> republican 0.8425926 0.1574074
#>
#> V12
#> response n y
#> democrat 0.8709677 0.1290323
#> republican 0.1481481 0.8518519
#>
#> V13
#> response n y
#> democrat 0.7096774 0.2903226
#> republican 0.1574074 0.8425926
#>
#> V14
#> response n y
#> democrat 0.65322581 0.34677419
#> republican 0.01851852 0.98148148
#>
#> V15
#> response n y
#> democrat 0.4032258 0.5967742
#> republican 0.8888889 0.1111111
#>
#> V16
#> response n y
#> democrat 0.05645161 0.94354839
#> republican 0.33333333 0.66666667
#>
confusion(house_nb) # Self-consistency
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> 232 items classified with 218 true positives (error rate = 6%)
#> Predicted
#> Actual 01 02 (sum) (FNR%)
#> 01 democrat 111 13 124 10
#> 02 republican 1 107 108 1
#> (sum) 112 120 232 6
confusion(cvpredict(house_nb), na.omit(HouseVotes84)$Class)
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V1'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V2'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V3'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V4'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V5'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V6'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V7'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V8'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V9'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V10'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V11'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V12'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V13'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V14'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V15'. Did you use factors with numeric labels for training, and numeric values for new data?
#> Warning: Type mismatch between training and new data for variable 'V16'. Did you use factors with numeric labels for training, and numeric values for new data?
#> 232 items classified with 217 true positives (error rate = 6.5%)
#> Predicted
#> Actual 01 02 (sum) (FNR%)
#> 01 democrat 110 14 124 11
#> 02 republican 1 107 108 1
#> (sum) 111 121 232 6