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Data X:
1 145 "'Male'" 1 130 "'Male'" 1 130 "'Female'" 1 120 "'Male'" 1 120 "'Female'" 1 140 "'Male'" 1 140 "'Female'" 1 120 "'Male'" 1 172 "'Male'" 1 150 "'Male'" 1 140 "'Male'" 1 130 "'Female'" 1 130 "'Male'" 1 110 "'Male'" 1 150 "'Female'" 1 120 "'Female'" 1 120 "'Female'" 1 150 "'Female'" 1 150 "'Male'" 1 140 "'Female'" 1 135 "'Male'" 1 130 "'Male'" 1 140 "'Male'" 1 150 "'Male'" 1 140 "'Male'" 1 160 "'Female'" 1 150 "'Male'" 1 110 "'Male'" 1 140 "'Female'" 1 130 "'Male'" 1 105 "'Female'" 1 120 "'Male'" 1 130 "'Male'" 1 125 "'Male'" 1 125 "'Male'" 1 142 "'Female'" 1 135 "'Female'" 1 150 "'Male'" 1 155 "'Female'" 1 160 "'Female'" 1 140 "'Female'" 1 130 "'Male'" 1 104 "'Male'" 1 130 "'Female'" 1 140 "'Male'" 1 120 "'Male'" 1 140 "'Male'" 1 138 "'Male'" 1 128 "'Female'" 1 138 "'Female'" 1 130 "'Female'" 1 120 "'Male'" 1 130 "'Male'" 1 108 "'Female'" 1 135 "'Female'" 1 134 "'Male'" 1 122 "'Male'" 1 115 "'Male'" 1 118 "'Male'" 1 128 "'Female'" 1 110 "'Female'" 1 108 "'Male'" 1 118 "'Male'" 1 135 "'Male'" 1 140 "'Male'" 1 138 "'Female'" 1 100 "'Male'" 1 130 "'Female'" 1 120 "'Male'" 1 124 "'Female'" 1 120 "'Male'" 1 94 "'Male'" 1 130 "'Male'" 1 140 "'Male'" 1 122 "'Female'" 1 135 "'Female'" 1 125 "'Male'" 1 140 "'Male'" 1 128 "'Male'" 1 105 "'Male'" 1 112 "'Male'" 1 128 "'Male'" 1 102 "'Female'" 1 152 "'Male'" 1 102 "'Female'" 1 115 "'Female'" 1 118 "'Male'" 1 101 "'Male'" 1 110 "'Female'" 1 100 "'Female'" 1 124 "'Male'" 1 132 "'Male'" 1 138 "'Male'" 1 132 "'Female'" 1 112 "'Female'" 1 142 "'Male'" 1 140 "'Female'" 1 108 "'Male'" 1 130 "'Male'" 1 130 "'Male'" 1 148 "'Male'" 1 178 "'Male'" 1 140 "'Female'" 1 120 "'Male'" 1 129 "'Male'" 1 120 "'Female'" 1 160 "'Male'" 1 138 "'Female'" 1 120 "'Female'" 1 110 "'Female'" 1 180 "'Female'" 1 150 "'Male'" 1 140 "'Female'" 1 110 "'Male'" 1 130 "'Male'" 1 120 "'Female'" 1 130 "'Male'" 1 120 "'Male'" 1 105 "'Female'" 1 138 "'Female'" 1 130 "'Female'" 1 138 "'Male'" 1 112 "'Female'" 1 108 "'Female'" 1 94 "'Female'" 1 118 "'Female'" 1 112 "'Male'" 1 152 "'Female'" 1 136 "'Female'" 1 120 "'Female'" 1 160 "'Female'" 1 134 "'Female'" 1 120 "'Male'" 1 110 "'Male'" 1 126 "'Female'" 1 130 "'Female'" 1 120 "'Female'" 1 128 "'Male'" 1 110 "'Male'" 1 128 "'Male'" 1 120 "'Female'" 1 115 "'Male'" 1 120 "'Female'" 1 106 "'Female'" 1 140 "'Female'" 1 156 "'Male'" 1 118 "'Female'" 1 150 "'Female'" 1 120 "'Male'" 1 130 "'Male'" 1 160 "'Male'" 1 112 "'Female'" 1 170 "'Male'" 1 146 "'Female'" 1 138 "'Female'" 1 130 "'Female'" 1 130 "'Male'" 1 122 "'Male'" 1 125 "'Male'" 1 130 "'Male'" 1 120 "'Male'" 1 132 "'Female'" 1 120 "'Male'" 1 138 "'Male'" 1 138 "'Male'" 0 160 "'Male'" 0 120 "'Male'" 0 140 "'Female'" 0 130 "'Male'" 0 140 "'Male'" 0 130 "'Male'" 0 110 "'Male'" 0 120 "'Male'" 0 132 "'Male'" 0 130 "'Male'" 0 110 "'Male'" 0 117 "'Male'" 0 140 "'Male'" 0 120 "'Male'" 0 150 "'Male'" 0 132 "'Male'" 0 150 "'Female'" 0 130 "'Female'" 0 112 "'Male'" 0 150 "'Male'" 0 112 "'Male'" 0 130 "'Male'" 0 124 "'Male'" 0 140 "'Male'" 0 110 "'Male'" 0 130 "'Female'" 0 128 "'Male'" 0 120 "'Male'" 0 145 "'Male'" 0 140 "'Male'" 0 170 "'Male'" 0 150 "'Male'" 0 125 "'Male'" 0 120 "'Male'" 0 110 "'Male'" 0 110 "'Male'" 0 125 "'Male'" 0 150 "'Male'" 0 180 "'Male'" 0 160 "'Female'" 0 128 "'Male'" 0 110 "'Male'" 0 150 "'Female'" 0 120 "'Male'" 0 140 "'Male'" 0 128 "'Male'" 0 120 "'Male'" 0 118 "'Male'" 0 145 "'Female'" 0 125 "'Male'" 0 132 "'Female'" 0 130 "'Female'" 0 130 "'Male'" 0 135 "'Male'" 0 130 "'Male'" 0 150 "'Female'" 0 140 "'Male'" 0 138 "'Male'" 0 200 "'Female'" 0 110 "'Male'" 0 145 "'Male'" 0 120 "'Male'" 0 120 "'Male'" 0 170 "'Male'" 0 125 "'Male'" 0 108 "'Male'" 0 165 "'Male'" 0 160 "'Male'" 0 120 "'Male'" 0 130 "'Male'" 0 140 "'Male'" 0 125 "'Male'" 0 140 "'Male'" 0 125 "'Male'" 0 126 "'Male'" 0 160 "'Male'" 0 174 "'Female'" 0 145 "'Male'" 0 152 "'Male'" 0 132 "'Male'" 0 124 "'Male'" 0 134 "'Female'" 0 160 "'Male'" 0 192 "'Male'" 0 140 "'Male'" 0 140 "'Male'" 0 132 "'Male'" 0 138 "'Female'" 0 100 "'Male'" 0 160 "'Male'" 0 142 "'Male'" 0 128 "'Male'" 0 144 "'Male'" 0 150 "'Female'" 0 120 "'Male'" 0 178 "'Female'" 0 112 "'Male'" 0 123 "'Male'" 0 108 "'Female'" 0 110 "'Male'" 0 112 "'Male'" 0 180 "'Female'" 0 118 "'Male'" 0 122 "'Male'" 0 130 "'Male'" 0 120 "'Male'" 0 134 "'Male'" 0 120 "'Male'" 0 100 "'Male'" 0 110 "'Male'" 0 125 "'Male'" 0 146 "'Male'" 0 124 "'Male'" 0 136 "'Female'" 0 138 "'Male'" 0 136 "'Male'" 0 128 "'Male'" 0 126 "'Male'" 0 152 "'Male'" 0 140 "'Male'" 0 140 "'Male'" 0 134 "'Male'" 0 154 "'Male'" 0 110 "'Male'" 0 128 "'Female'" 0 148 "'Male'" 0 114 "'Male'" 0 170 "'Female'" 0 152 "'Male'" 0 120 "'Male'" 0 140 "'Male'" 0 124 "'Female'" 0 164 "'Male'" 0 140 "'Female'" 0 110 "'Male'" 0 144 "'Male'" 0 130 "'Male'" 0 130 "'Female'"
Names of X columns:
target bloodpressureNum sexLabel
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Target
(?)
kernel
(?)
gaussian
rectangular
triangular
epanechnikov
biweight
cosine
optcosine
Categorical (c) or Numeric (n)
(?)
Repeated CV
(?)
no
yes
Chart options
R Code
par4 <- 'no' par3 <- 'cnc' par2 <- 'gaussian' par1 <- '3' .libPaths() library(caret) library(naivebayes) mywarning <- '' par1 <- as.numeric(par1) if(is.na(par1)) { par1 <- 1 mywarning = 'Warning: you did not specify the column number of the target! The first column was selected by default.' } print.naive_bayes <- function (x, ...) { model <- 'Naive Bayes' n_char <- getOption('width') str_left_right <- paste0(rep('=', floor((n_char - nchar(model)) / 2)), collapse = '') str_full <- paste0(str_left_right, ' ', model, ' ', ifelse(n_char %% 2 != 0, '=', ''), str_left_right) len <- nchar(str_full) l <- paste0(rep('-', len), collapse = '') cat('\n') cat(str_full, '\n', '\n', 'Call:', '\n') print(x$call) cat('\n') cat(l, '\n', '\n') cat( 'Laplace smoothing:', x$laplace) cat('\n') cat('\n') cat(l, '\n', '\n') cat(' A priori probabilities:', '\n') print(x$prior) cat('\n') cat(l, '\n', '\n') cat(' Tables:', '\n') tabs <- x$tables n <- length(x$tables) indices <- seq_len(min(25,n)) tabs <- tabs[indices] print(tabs) if (n > 25) { cat('\n\n') cat('# ... and', n - 25, ifelse(n - 25 == 1, 'more table\n\n', 'more tables\n\n')) cat(l) } cat('\n\n') } x <- na.omit(data.frame(t(x))) k <- length(x[1,]) n <- length(x[,1]) x <- as.data.frame(x) for(ii in 1:k) { if(substr(par3,ii,ii) == 'c') x[,ii] <- as.character(x[,ii]) if(substr(par3,ii,ii) == 'n') x[,ii] <- as.numeric(x[,ii]) } myf <- formula(paste(colnames(x)[par1],' ~ .',sep='')) myf nb_grid <- expand.grid(usekernel = c(TRUE, FALSE), laplace = c(0, 0.5, 1, 2, 3, 4), adjust = c(0.75, 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5)) fitControl <- trainControl(method = 'repeatedcv', number = 10, repeats = 5) if(par4=='no') { naive_bayes_via_caret <- train(myf, data = x, method = 'naive_bayes', kernel = par2, bw = 'SJ', usepoisson = TRUE, tuneGrid = nb_grid) } if(par4=='yes') { naive_bayes_via_caret <- train(myf, data = x, method = 'naive_bayes', kernel = par2, bw = 'SJ', usepoisson = TRUE, tuneGrid = nb_grid, trControl = fitControl) } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Naive Bayes Classifier',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('naive_bayes_via_caret$results'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('naive_bayes_via_caret$finalModel$tuneValue'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('print.naive_bayes(naive_bayes_via_caret$finalModel)'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) z <- cbind(x, predict(naive_bayes_via_caret$finalModel, x, type = 'prob')) a<-table.element(a,paste('<pre>',RC.texteval('z'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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