Perform the SMS spam filtering analysis.
Step 1: Load data
Step 2: Exploring and preparing the data
# read the sms data into the sms data frame
sms_raw <- read.csv("C:/Users/Bhawna/Documents/blog/data/sms_spam.csv", stringsAsFactors = FALSE)
# examine the structure of the sms data
str(sms_raw)## 'data.frame': 5559 obs. of 2 variables:
## $ type: chr "ham" "ham" "ham" "spam" ...
## $ text: chr "Hope you are having a good week. Just checking in" "K..give back my thanks." "Am also doing in cbe only. But have to pay." "complimentary 4 STAR Ibiza Holiday or £10,000 cash needs your URGENT collection. 09066364349 NOW from Landline not to lose out"| __truncated__ ...# convert spam/ham to factor.
sms_raw$type <- factor(sms_raw$type)
# examine the type variable more carefully
str(sms_raw$type)## Factor w/ 2 levels "ham","spam": 1 1 1 2 2 1 1 1 2 1 ...table(sms_raw$type)##
## ham spam
## 4812 747# build a corpus using the text mining (tm) package
library(tm)## Loading required package: NLPsms_corpus <- VCorpus(VectorSource(sms_raw$text))
# examine the sms corpus
print(sms_corpus)## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 5559inspect(sms_corpus[1:2])## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 2
##
## [[1]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 49
##
## [[2]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 23as.character(sms_corpus[[1]])## [1] "Hope you are having a good week. Just checking in"lapply(sms_corpus[1:2], as.character)## $`1`
## [1] "Hope you are having a good week. Just checking in"
##
## $`2`
## [1] "K..give back my thanks."# clean up the corpus using tm_map()
sms_corpus_clean <- tm_map(sms_corpus, content_transformer(tolower))
# show the difference between sms_corpus and corpus_clean
as.character(sms_corpus[[1]])## [1] "Hope you are having a good week. Just checking in"as.character(sms_corpus_clean[[1]])## [1] "hope you are having a good week. just checking in"sms_corpus_clean <- tm_map(sms_corpus_clean, removeNumbers) # remove numbers
sms_corpus_clean <- tm_map(sms_corpus_clean, removeWords, stopwords()) # remove stop words
sms_corpus_clean <- tm_map(sms_corpus_clean, removePunctuation) # remove punctuation# tip: create a custom function to replace (rather than remove) punctuation
removePunctuation("hello...world")## [1] "helloworld"replacePunctuation <- function(x) { gsub("[[:punct:]]+", " ", x) }
replacePunctuation("hello...world")## [1] "hello world"# illustration of word stemming
library(SnowballC)
wordStem(c("learn", "learned", "learning", "learns"))## [1] "learn" "learn" "learn" "learn"sms_corpus_clean <- tm_map(sms_corpus_clean, stemDocument)
sms_corpus_clean <- tm_map(sms_corpus_clean, stripWhitespace) # eliminate unneeded whitespace
# examine the final clean corpus
lapply(sms_corpus[1:3], as.character)## $`1`
## [1] "Hope you are having a good week. Just checking in"
##
## $`2`
## [1] "K..give back my thanks."
##
## $`3`
## [1] "Am also doing in cbe only. But have to pay."lapply(sms_corpus_clean[1:3], as.character)## $`1`
## [1] "hope good week just check"
##
## $`2`
## [1] "kgive back thank"
##
## $`3`
## [1] " also cbe pay"# create a document-term sparse matrix
sms_dtm <- DocumentTermMatrix(sms_corpus_clean)
# alternative solution: create a document-term sparse matrix directly from the SMS corpus
sms_dtm2 <- DocumentTermMatrix(sms_corpus, control = list(
tolower = TRUE,
removeNumbers = TRUE,
stopwords = TRUE,
removePunctuation = TRUE,
stemming = TRUE
))
# alternative solution: using custom stop words function ensures identical result
sms_dtm3 <- DocumentTermMatrix(sms_corpus, control = list(
tolower = TRUE,
removeNumbers = TRUE,
stopwords = function(x) { removeWords(x, stopwords()) },
removePunctuation = TRUE,
stemming = TRUE
))
# compare the result
sms_dtm## <<DocumentTermMatrix (documents: 5559, terms: 6576)>>
## Non-/sparse entries: 42173/36513811
## Sparsity : 100%
## Maximal term length: 40
## Weighting : term frequency (tf)sms_dtm2## <<DocumentTermMatrix (documents: 5559, terms: 6965)>>
## Non-/sparse entries: 43231/38675204
## Sparsity : 100%
## Maximal term length: 40
## Weighting : term frequency (tf)sms_dtm3## <<DocumentTermMatrix (documents: 5559, terms: 6576)>>
## Non-/sparse entries: 42173/36513811
## Sparsity : 100%
## Maximal term length: 40
## Weighting : term frequency (tf)# creating training and test datasets
sms_dtm_train <- sms_dtm[1:4169, ]
sms_dtm_test <- sms_dtm[4170:5559, ]
# also save the labels
sms_train_labels <- sms_raw[1:4169, ]$type
sms_test_labels <- sms_raw[4170:5559, ]$type
# check that the proportion of spam is similar
prop.table(table(sms_train_labels))## sms_train_labels
## ham spam
## 0.8647158 0.1352842prop.table(table(sms_test_labels))## sms_test_labels
## ham spam
## 0.8683453 0.1316547# word cloud visualization
library(wordcloud)## Loading required package: methods## Loading required package: RColorBrewerwordcloud(sms_corpus_clean, min.freq = 50, random.order = FALSE)
# subset the training data into spam and ham groups
spam <- subset(sms_raw, type == "spam")
ham <- subset(sms_raw, type == "ham")
wordcloud(spam$text, max.words = 40, scale = c(3, 0.5))
wordcloud(ham$text, max.words = 40, scale = c(3, 0.5))
sms_dtm_freq_train <- removeSparseTerms(sms_dtm_train, 0.999)
sms_dtm_freq_train## <<DocumentTermMatrix (documents: 4169, terms: 1104)>>
## Non-/sparse entries: 24827/4577749
## Sparsity : 99%
## Maximal term length: 19
## Weighting : term frequency (tf)# indicator features for frequent words
findFreqTerms(sms_dtm_train, 5)## [1] "ââ" "abiola" "abl"
## [4] "abt" "accept" "access"
## [7] "account" "across" "act"
## [10] "activ" "actual" "add"
## [13] "address" "admir" "adult"
## [16] "advanc" "aft" "afternoon"
## [19] "age" "ago" "aha"
## [22] "ahead" "aight" "aint"
## [25] "air" "aiyo" "alex"
## [28] "almost" "alon" "alreadi"
## [31] "alright" "also" "alway"
## [34] "angri" "announc" "anoth"
## [37] "answer" "anymor" "anyon"
## [40] "anyth" "anytim" "anyway"
## [43] "apart" "app" "appli"
## [46] "appreci" "arcad" "ard"
## [49] "area" "argu" "argument"
## [52] "armand" "around" "arrang"
## [55] "arriv" "asap" "ask"
## [58] "askd" "attempt" "auction"
## [61] "avail" "ave" "avoid"
## [64] "await" "awak" "award"
## [67] "away" "awesom" "âwk"
## [70] "babe" "babi" "back"
## [73] "bad" "bag" "bank"
## [76] "bare" "basic" "bath"
## [79] "batteri" "bcoz" "bday"
## [82] "beauti" "becom" "bed"
## [85] "bedroom" "beer" "begin"
## [88] "believ" "best" "better"
## [91] "bid" "big" "bill"
## [94] "bird" "birthday" "bit"
## [97] "black" "blank" "bless"
## [100] "blue" "bluetooth" "bold"
## [103] "bonus" "boo" "book"
## [106] "boost" "bore" "boss"
## [109] "bother" "bout" "box"
## [112] "boy" "boytoy" "break"
## [115] "breath" "bring" "brother"
## [118] "bslvyl" "btnationalr" "buck"
## [121] "bus" "busi" "buy"
## [124] "cabin" "call" "caller"
## [127] "callertun" "camcord" "came"
## [130] "camera" "campus" "can"
## [133] "cancel" "cancer" "cant"
## [136] "car" "card" "care"
## [139] "carlo" "case" "cash"
## [142] "cashbal" "catch" "caus"
## [145] "celebr" "cell" "centr"
## [148] "chanc" "chang" "charg"
## [151] "chat" "cheap" "cheaper"
## [154] "check" "cheer" "chennai"
## [157] "chikku" "childish" "children"
## [160] "choic" "choos" "christma"
## [163] "claim" "class" "clean"
## [166] "clear" "close" "club"
## [169] "code" "coffe" "cold"
## [172] "colleagu" "collect" "colleg"
## [175] "colour" "come" "comin"
## [178] "comp" "compani" "competit"
## [181] "complet" "complimentari" "comput"
## [184] "condit" "confirm" "congrat"
## [187] "congratul" "connect" "contact"
## [190] "content" "contract" "cook"
## [193] "cool" "copi" "correct"
## [196] "cos" "cost" "costa"
## [199] "costâpm" "coupl" "cours"
## [202] "cover" "coz" "crave"
## [205] "crazi" "creat" "credit"
## [208] "cri" "cross" "cuddl"
## [211] "cum" "cup" "current"
## [214] "custcar" "custom" "cut"
## [217] "cute" "cuz" "dad"
## [220] "daddi" "darl" "darlin"
## [223] "darren" "dat" "date"
## [226] "day" "dead" "deal"
## [229] "dear" "decid" "decim"
## [232] "decis" "deep" "definit"
## [235] "del" "deliv" "deliveri"
## [238] "den" "depend" "detail"
## [241] "didnt" "die" "diet"
## [244] "differ" "difficult" "digit"
## [247] "din" "dinner" "direct"
## [250] "dis" "discount" "discuss"
## [253] "disturb" "dnt" "doc"
## [256] "doctor" "doesnt" "dog"
## [259] "doin" "don" "done"
## [262] "dont" "door" "doubl"
## [265] "download" "draw" "dream"
## [268] "drink" "drive" "drop"
## [271] "drug" "dude" "due"
## [274] "dun" "dunno" "dvd"
## [277] "earli" "earlier" "earth"
## [280] "easi" "eat" "eatin"
## [283] "egg" "either" "els"
## [286] "email" "embarass" "end"
## [289] "energi" "england" "enjoy"
## [292] "enough" "enter" "entitl"
## [295] "entri" "envelop" "etc"
## [298] "euro" "eve" "even"
## [301] "ever" "everi" "everybodi"
## [304] "everyon" "everyth" "exact"
## [307] "exam" "excel" "excit"
## [310] "excus" "expect" "experi"
## [313] "expir" "extra" "eye"
## [316] "face" "facebook" "fact"
## [319] "fall" "famili" "fanci"
## [322] "fantasi" "fantast" "far"
## [325] "fast" "fat" "father"
## [328] "fault" "feb" "feel"
## [331] "felt" "fetch" "fight"
## [334] "figur" "file" "fill"
## [337] "film" "final" "find"
## [340] "fine" "finger" "finish"
## [343] "first" "fix" "flag"
## [346] "flat" "flight" "flower"
## [349] "follow" "fone" "food"
## [352] "forev" "forget" "forgot"
## [355] "forward" "found" "freak"
## [358] "free" "freemsg" "freephon"
## [361] "fren" "fri" "friday"
## [364] "friend" "friendship" "frm"
## [367] "frnd" "frnds" "full"
## [370] "fullonsmscom" "fun" "funni"
## [373] "futur" "gal" "game"
## [376] "gap" "gas" "gave"
## [379] "gay" "gentl" "get"
## [382] "gettin" "gift" "girl"
## [385] "girlfrnd" "give" "glad"
## [388] "god" "goe" "goin"
## [391] "gone" "gonna" "good"
## [394] "goodmorn" "goodnight" "got"
## [397] "goto" "gotta" "great"
## [400] "grin" "guarante" "gud"
## [403] "guess" "guy" "gym"
## [406] "haf" "haha" "hai"
## [409] "hair" "half" "hand"
## [412] "handset" "hang" "happen"
## [415] "happi" "hard" "hate"
## [418] "hav" "havent" "head"
## [421] "hear" "heard" "heart"
## [424] "heavi" "hee" "hell"
## [427] "hello" "help" "hey"
## [430] "hgsuiteland" "hit" "hiya"
## [433] "hmm" "hmmm" "hmv"
## [436] "hol" "hold" "holder"
## [439] "holiday" "home" "hook"
## [442] "hop" "hope" "horni"
## [445] "hospit" "hot" "hotel"
## [448] "hour" "hous" "how"
## [451] "howev" "howz" "hrs"
## [454] "httpwwwurawinnercom" "hug" "huh"
## [457] "hungri" "hurri" "hurt"
## [460] "iâëm" "ice" "idea"
## [463] "identifi" "ignor" "ill"
## [466] "immedi" "import" "inc"
## [469] "includ" "india" "info"
## [472] "inform" "insid" "instead"
## [475] "interest" "invit" "ipod"
## [478] "irrit" "ish" "island"
## [481] "issu" "ive" "izzit"
## [484] "januari" "jay" "job"
## [487] "john" "join" "joke"
## [490] "joy" "jst" "jus"
## [493] "just" "juz" "kate"
## [496] "keep" "kept" "kick"
## [499] "kid" "kill" "kind"
## [502] "kinda" "king" "kiss"
## [505] "knew" "know" "knw"
## [508] "ladi" "land" "landlin"
## [511] "laptop" "lar" "last"
## [514] "late" "later" "latest"
## [517] "laugh" "lazi" "ldn"
## [520] "lead" "learn" "least"
## [523] "leav" "lect" "left"
## [526] "leh" "lei" "less"
## [529] "lesson" "let" "letter"
## [532] "liao" "librari" "lie"
## [535] "life" "lift" "light"
## [538] "like" "line" "link"
## [541] "list" "listen" "littl"
## [544] "live" "lmao" "load"
## [547] "loan" "local" "locat"
## [550] "log" "lol" "london"
## [553] "long" "longer" "look"
## [556] "lookin" "lor" "lose"
## [559] "lost" "lot" "lovabl"
## [562] "love" "lover" "loyalti"
## [565] "ltd" "luck" "lucki"
## [568] "lunch" "luv" "mad"
## [571] "made" "mah" "mail"
## [574] "make" "malaria" "man"
## [577] "mani" "march" "mark"
## [580] "marri" "match" "mate"
## [583] "matter" "maxim" "maxmin"
## [586] "may" "mayb" "meal"
## [589] "mean" "meant" "med"
## [592] "medic" "meet" "meetin"
## [595] "meh" "member" "men"
## [598] "merri" "messag" "met"
## [601] "mid" "midnight" "might"
## [604] "min" "mind" "mine"
## [607] "minut" "miracl" "miss"
## [610] "mistak" "moan" "mob"
## [613] "mobil" "mobileupd" "mode"
## [616] "mom" "moment" "mon"
## [619] "monday" "money" "month"
## [622] "morn" "mother" "motorola"
## [625] "move" "movi" "mrng"
## [628] "mrt" "mrw" "msg"
## [631] "msgs" "mths" "much"
## [634] "mum" "murder" "music"
## [637] "must" "muz" "nah"
## [640] "nake" "name" "nation"
## [643] "natur" "naughti" "near"
## [646] "need" "net" "network"
## [649] "neva" "never" "new"
## [652] "news" "next" "nice"
## [655] "nigeria" "night" "nite"
## [658] "nobodi" "noe" "nokia"
## [661] "noon" "nope" "normal"
## [664] "normpton" "noth" "notic"
## [667] "now" "num" "number"
## [670] "nyt" "obvious" "offer"
## [673] "offic" "offici" "okay"
## [676] "oki" "old" "omg"
## [679] "one" "onlin" "onto"
## [682] "oop" "open" "oper"
## [685] "opinion" "opt" "optout"
## [688] "orang" "orchard" "order"
## [691] "oredi" "oso" "other"
## [694] "otherwis" "outsid" "pack"
## [697] "page" "paid" "pain"
## [700] "paper" "parent" "park"
## [703] "part" "parti" "partner"
## [706] "pass" "passion" "password"
## [709] "past" "pay" "peopl"
## [712] "per" "person" "pete"
## [715] "phone" "photo" "pic"
## [718] "pick" "pictur" "pin"
## [721] "piss" "pix" "pizza"
## [724] "place" "plan" "play"
## [727] "player" "pleas" "pleasur"
## [730] "plenti" "pls" "plus"
## [733] "plz" "pmin" "pmsg"
## [736] "pobox" "point" "poli"
## [739] "polic" "poor" "pop"
## [742] "possess" "possibl" "post"
## [745] "pound" "power" "ppm"
## [748] "pray" "present" "press"
## [751] "pretti" "previous" "price"
## [754] "princess" "privat" "prize"
## [757] "prob" "probabl" "problem"
## [760] "project" "promis" "pub"
## [763] "put" "qualiti" "question"
## [766] "quick" "quit" "quiz"
## [769] "quot" "rain" "random"
## [772] "rang" "rate" "rather"
## [775] "rcvd" "reach" "read"
## [778] "readi" "real" "reali"
## [781] "realli" "reason" "receipt"
## [784] "receiv" "recent" "record"
## [787] "refer" "regard" "regist"
## [790] "relat" "relax" "remain"
## [793] "rememb" "remind" "remov"
## [796] "rent" "rental" "repli"
## [799] "repres" "request" "respond"
## [802] "respons" "rest" "result"
## [805] "return" "reveal" "review"
## [808] "reward" "right" "ring"
## [811] "rington" "rite" "road"
## [814] "rock" "role" "room"
## [817] "roommat" "rose" "round"
## [820] "rowwjhl" "rpli" "rreveal"
## [823] "run" "rush" "sad"
## [826] "sae" "safe" "said"
## [829] "sale" "sat" "saturday"
## [832] "savamob" "save" "saw"
## [835] "say" "sch" "school"
## [838] "scream" "sea" "search"
## [841] "sec" "second" "secret"
## [844] "see" "seem" "seen"
## [847] "select" "self" "sell"
## [850] "semest" "send" "sens"
## [853] "sent" "serious" "servic"
## [856] "set" "settl" "sex"
## [859] "sexi" "shall" "share"
## [862] "shd" "ship" "shirt"
## [865] "shop" "short" "show"
## [868] "shower" "sick" "side"
## [871] "sigh" "sight" "sign"
## [874] "silent" "simpl" "sinc"
## [877] "singl" "sipix" "sir"
## [880] "sis" "sister" "sit"
## [883] "situat" "skxh" "skype"
## [886] "slave" "sleep" "slept"
## [889] "slow" "slowli" "small"
## [892] "smile" "smoke" "sms"
## [895] "smth" "snow" "sofa"
## [898] "sol" "somebodi" "someon"
## [901] "someth" "sometim" "somewher"
## [904] "song" "soni" "sonyericsson"
## [907] "soon" "sorri" "sort"
## [910] "sound" "south" "space"
## [913] "speak" "special" "specialcal"
## [916] "spend" "spent" "spoke"
## [919] "spree" "stand" "start"
## [922] "statement" "station" "stay"
## [925] "std" "step" "still"
## [928] "stockport" "stone" "stop"
## [931] "store" "stori" "street"
## [934] "student" "studi" "stuff"
## [937] "stupid" "style" "sub"
## [940] "subscrib" "success" "suck"
## [943] "suit" "summer" "sun"
## [946] "sunday" "sunshin" "sup"
## [949] "support" "suppos" "sure"
## [952] "surf" "surpris" "sweet"
## [955] "swing" "system" "take"
## [958] "talk" "tampa" "tariff"
## [961] "tcs" "tea" "teach"
## [964] "tear" "teas" "tel"
## [967] "tell" "ten" "tenerif"
## [970] "term" "test" "text"
## [973] "thank" "thanx" "that"
## [976] "thing" "think" "thinkin"
## [979] "thk" "tho" "though"
## [982] "thought" "throw" "thru"
## [985] "tht" "thur" "tick"
## [988] "ticket" "til" "till"
## [991] "time" "tire" "titl"
## [994] "tmr" "toclaim" "today"
## [997] "togeth" "told" "tomo"
## [1000] "tomorrow" "tone" "tonight"
## [1003] "tonit" "took" "top"
## [1006] "torch" "tot" "total"
## [1009] "touch" "tough" "tour"
## [1012] "toward" "town" "track"
## [1015] "train" "transact" "travel"
## [1018] "treat" "tri" "trip"
## [1021] "troubl" "true" "trust"
## [1024] "truth" "tscs" "ttyl"
## [1027] "tuesday" "turn" "twice"
## [1030] "two" "txt" "txting"
## [1033] "txts" "type" "ufind"
## [1036] "ugh" "ull" "uncl"
## [1039] "understand" "unless" "unlimit"
## [1042] "unredeem" "unsub" "unsubscrib"
## [1045] "updat" "ure" "urgent"
## [1048] "urself" "use" "user"
## [1051] "usf" "usual" "uve"
## [1054] "valentin" "valid" "valu"
## [1057] "via" "video" "vikki"
## [1060] "visit" "vodafon" "voic"
## [1063] "vomit" "voucher" "wait"
## [1066] "wake" "walk" "wan"
## [1069] "wana" "wanna" "want"
## [1072] "wap" "warm" "wast"
## [1075] "wat" "watch" "water"
## [1078] "way" "weak" "wear"
## [1081] "weather" "wed" "wednesday"
## [1084] "weed" "week" "weekend"
## [1087] "welcom" "well" "wen"
## [1090] "went" "what" "whatev"
## [1093] "whenev" "whole" "wid"
## [1096] "wif" "wife" "wil"
## [1099] "will" "win" "wine"
## [1102] "winner" "wish" "wit"
## [1105] "within" "without" "wiv"
## [1108] "wkli" "wks" "wnt"
## [1111] "woke" "won" "wonder"
## [1114] "wont" "word" "work"
## [1117] "workin" "world" "worri"
## [1120] "wors" "worth" "wot"
## [1123] "wow" "write" "wrong"
## [1126] "wwq" "wwwgetzedcouk" "xmas"
## [1129] "xxx" "yahoo" "yar"
## [1132] "yeah" "year" "yep"
## [1135] "yes" "yesterday" "yet"
## [1138] "yoga" "yup"# save frequently-appearing terms to a character vector
sms_freq_words <- findFreqTerms(sms_dtm_train, 5)
str(sms_freq_words)## chr [1:1139] "ââ""| __truncated__ "abiola" "abl" "abt" "accept" ...# create DTMs with only the frequent terms
sms_dtm_freq_train <- sms_dtm_train[ , sms_freq_words]
sms_dtm_freq_test <- sms_dtm_test[ , sms_freq_words]
# convert counts to a factor
convert_counts <- function(x) {
x <- ifelse(x > 0, "Yes", "No")
}
# apply() convert_counts() to columns of train/test data
sms_train <- apply(sms_dtm_freq_train, MARGIN = 2, convert_counts)
sms_test <- apply(sms_dtm_freq_test, MARGIN = 2, convert_counts)Step 3: Training a model on the data —-
library(e1071)
sms_classifier <- naiveBayes(sms_train, sms_train_labels)Step 4: Evaluating model performance —-
sms_test_pred <- predict(sms_classifier, sms_test)
head(sms_test_pred)## [1] ham ham ham ham spam ham
## Levels: ham spamlibrary(gmodels)
CrossTable(sms_test_pred, sms_test_labels,
prop.chisq = FALSE, prop.t = FALSE, prop.r = FALSE,
dnn = c('predicted', 'actual'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 1390
##
##
## | actual
## predicted | ham | spam | Row Total |
## -------------|-----------|-----------|-----------|
## ham | 1201 | 30 | 1231 |
## | 0.995 | 0.164 | |
## -------------|-----------|-----------|-----------|
## spam | 6 | 153 | 159 |
## | 0.005 | 0.836 | |
## -------------|-----------|-----------|-----------|
## Column Total | 1207 | 183 | 1390 |
## | 0.868 | 0.132 | |
## -------------|-----------|-----------|-----------|
##
## Step 5: Improving model performance —-
sms_classifier2 <- naiveBayes(sms_train, sms_train_labels, laplace = 1)
sms_test_pred2 <- predict(sms_classifier2, sms_test)
CrossTable(sms_test_pred2, sms_test_labels,
prop.chisq = FALSE, prop.t = FALSE, prop.r = FALSE,
dnn = c('predicted', 'actual'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 1390
##
##
## | actual
## predicted | ham | spam | Row Total |
## -------------|-----------|-----------|-----------|
## ham | 1202 | 28 | 1230 |
## | 0.996 | 0.153 | |
## -------------|-----------|-----------|-----------|
## spam | 5 | 155 | 160 |
## | 0.004 | 0.847 | |
## -------------|-----------|-----------|-----------|
## Column Total | 1207 | 183 | 1390 |
## | 0.868 | 0.132 | |
## -------------|-----------|-----------|-----------|
##
## 
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