Vemos como crear un modelo GLM (General Lineal Model) para clasificar mail Spam / No spam En primer lugar, cargamos las librerias a utilizar.
suppressMessages(library(caret))
suppressMessages(library(kernlab))
suppressMessages(library(e1071))
suppressMessages(library(sjstats))## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.2.14
## Current Matrix version is 1.2.12
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' packageExploramos los datos “spam”
data(spam)
head(spam)## make address all num3d our over remove internet order mail receive
## 1 0.00 0.64 0.64 0 0.32 0.00 0.00 0.00 0.00 0.00 0.00
## 2 0.21 0.28 0.50 0 0.14 0.28 0.21 0.07 0.00 0.94 0.21
## 3 0.06 0.00 0.71 0 1.23 0.19 0.19 0.12 0.64 0.25 0.38
## 4 0.00 0.00 0.00 0 0.63 0.00 0.31 0.63 0.31 0.63 0.31
## 5 0.00 0.00 0.00 0 0.63 0.00 0.31 0.63 0.31 0.63 0.31
## 6 0.00 0.00 0.00 0 1.85 0.00 0.00 1.85 0.00 0.00 0.00
## will people report addresses free business email you credit your font
## 1 0.64 0.00 0.00 0.00 0.32 0.00 1.29 1.93 0.00 0.96 0
## 2 0.79 0.65 0.21 0.14 0.14 0.07 0.28 3.47 0.00 1.59 0
## 3 0.45 0.12 0.00 1.75 0.06 0.06 1.03 1.36 0.32 0.51 0
## 4 0.31 0.31 0.00 0.00 0.31 0.00 0.00 3.18 0.00 0.31 0
## 5 0.31 0.31 0.00 0.00 0.31 0.00 0.00 3.18 0.00 0.31 0
## 6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0
## num000 money hp hpl george num650 lab labs telnet num857 data num415
## 1 0.00 0.00 0 0 0 0 0 0 0 0 0 0
## 2 0.43 0.43 0 0 0 0 0 0 0 0 0 0
## 3 1.16 0.06 0 0 0 0 0 0 0 0 0 0
## 4 0.00 0.00 0 0 0 0 0 0 0 0 0 0
## 5 0.00 0.00 0 0 0 0 0 0 0 0 0 0
## 6 0.00 0.00 0 0 0 0 0 0 0 0 0 0
## num85 technology num1999 parts pm direct cs meeting original project
## 1 0 0 0.00 0 0 0.00 0 0 0.00 0
## 2 0 0 0.07 0 0 0.00 0 0 0.00 0
## 3 0 0 0.00 0 0 0.06 0 0 0.12 0
## 4 0 0 0.00 0 0 0.00 0 0 0.00 0
## 5 0 0 0.00 0 0 0.00 0 0 0.00 0
## 6 0 0 0.00 0 0 0.00 0 0 0.00 0
## re edu table conference charSemicolon charRoundbracket
## 1 0.00 0.00 0 0 0.00 0.000
## 2 0.00 0.00 0 0 0.00 0.132
## 3 0.06 0.06 0 0 0.01 0.143
## 4 0.00 0.00 0 0 0.00 0.137
## 5 0.00 0.00 0 0 0.00 0.135
## 6 0.00 0.00 0 0 0.00 0.223
## charSquarebracket charExclamation charDollar charHash capitalAve
## 1 0 0.778 0.000 0.000 3.756
## 2 0 0.372 0.180 0.048 5.114
## 3 0 0.276 0.184 0.010 9.821
## 4 0 0.137 0.000 0.000 3.537
## 5 0 0.135 0.000 0.000 3.537
## 6 0 0.000 0.000 0.000 3.000
## capitalLong capitalTotal type
## 1 61 278 spam
## 2 101 1028 spam
## 3 485 2259 spam
## 4 40 191 spam
## 5 40 191 spam
## 6 15 54 spamstr(spam)## 'data.frame': 4601 obs. of 58 variables:
## $ make : num 0 0.21 0.06 0 0 0 0 0 0.15 0.06 ...
## $ address : num 0.64 0.28 0 0 0 0 0 0 0 0.12 ...
## $ all : num 0.64 0.5 0.71 0 0 0 0 0 0.46 0.77 ...
## $ num3d : num 0 0 0 0 0 0 0 0 0 0 ...
## $ our : num 0.32 0.14 1.23 0.63 0.63 1.85 1.92 1.88 0.61 0.19 ...
## $ over : num 0 0.28 0.19 0 0 0 0 0 0 0.32 ...
## $ remove : num 0 0.21 0.19 0.31 0.31 0 0 0 0.3 0.38 ...
## $ internet : num 0 0.07 0.12 0.63 0.63 1.85 0 1.88 0 0 ...
## $ order : num 0 0 0.64 0.31 0.31 0 0 0 0.92 0.06 ...
## $ mail : num 0 0.94 0.25 0.63 0.63 0 0.64 0 0.76 0 ...
## $ receive : num 0 0.21 0.38 0.31 0.31 0 0.96 0 0.76 0 ...
## $ will : num 0.64 0.79 0.45 0.31 0.31 0 1.28 0 0.92 0.64 ...
## $ people : num 0 0.65 0.12 0.31 0.31 0 0 0 0 0.25 ...
## $ report : num 0 0.21 0 0 0 0 0 0 0 0 ...
## $ addresses : num 0 0.14 1.75 0 0 0 0 0 0 0.12 ...
## $ free : num 0.32 0.14 0.06 0.31 0.31 0 0.96 0 0 0 ...
## $ business : num 0 0.07 0.06 0 0 0 0 0 0 0 ...
## $ email : num 1.29 0.28 1.03 0 0 0 0.32 0 0.15 0.12 ...
## $ you : num 1.93 3.47 1.36 3.18 3.18 0 3.85 0 1.23 1.67 ...
## $ credit : num 0 0 0.32 0 0 0 0 0 3.53 0.06 ...
## $ your : num 0.96 1.59 0.51 0.31 0.31 0 0.64 0 2 0.71 ...
## $ font : num 0 0 0 0 0 0 0 0 0 0 ...
## $ num000 : num 0 0.43 1.16 0 0 0 0 0 0 0.19 ...
## $ money : num 0 0.43 0.06 0 0 0 0 0 0.15 0 ...
## $ hp : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hpl : num 0 0 0 0 0 0 0 0 0 0 ...
## $ george : num 0 0 0 0 0 0 0 0 0 0 ...
## $ num650 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ lab : num 0 0 0 0 0 0 0 0 0 0 ...
## $ labs : num 0 0 0 0 0 0 0 0 0 0 ...
## $ telnet : num 0 0 0 0 0 0 0 0 0 0 ...
## $ num857 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ data : num 0 0 0 0 0 0 0 0 0.15 0 ...
## $ num415 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ num85 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ technology : num 0 0 0 0 0 0 0 0 0 0 ...
## $ num1999 : num 0 0.07 0 0 0 0 0 0 0 0 ...
## $ parts : num 0 0 0 0 0 0 0 0 0 0 ...
## $ pm : num 0 0 0 0 0 0 0 0 0 0 ...
## $ direct : num 0 0 0.06 0 0 0 0 0 0 0 ...
## $ cs : num 0 0 0 0 0 0 0 0 0 0 ...
## $ meeting : num 0 0 0 0 0 0 0 0 0 0 ...
## $ original : num 0 0 0.12 0 0 0 0 0 0.3 0 ...
## $ project : num 0 0 0 0 0 0 0 0 0 0.06 ...
## $ re : num 0 0 0.06 0 0 0 0 0 0 0 ...
## $ edu : num 0 0 0.06 0 0 0 0 0 0 0 ...
## $ table : num 0 0 0 0 0 0 0 0 0 0 ...
## $ conference : num 0 0 0 0 0 0 0 0 0 0 ...
## $ charSemicolon : num 0 0 0.01 0 0 0 0 0 0 0.04 ...
## $ charRoundbracket : num 0 0.132 0.143 0.137 0.135 0.223 0.054 0.206 0.271 0.03 ...
## $ charSquarebracket: num 0 0 0 0 0 0 0 0 0 0 ...
## $ charExclamation : num 0.778 0.372 0.276 0.137 0.135 0 0.164 0 0.181 0.244 ...
## $ charDollar : num 0 0.18 0.184 0 0 0 0.054 0 0.203 0.081 ...
## $ charHash : num 0 0.048 0.01 0 0 0 0 0 0.022 0 ...
## $ capitalAve : num 3.76 5.11 9.82 3.54 3.54 ...
## $ capitalLong : num 61 101 485 40 40 15 4 11 445 43 ...
## $ capitalTotal : num 278 1028 2259 191 191 ...
## $ type : Factor w/ 2 levels "nonspam","spam": 2 2 2 2 2 2 2 2 2 2 ...summary(spam)## make address all num3d
## Min. :0.0000 Min. : 0.000 Min. :0.0000 Min. : 0.00000
## 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 0.00000
## Median :0.0000 Median : 0.000 Median :0.0000 Median : 0.00000
## Mean :0.1046 Mean : 0.213 Mean :0.2807 Mean : 0.06542
## 3rd Qu.:0.0000 3rd Qu.: 0.000 3rd Qu.:0.4200 3rd Qu.: 0.00000
## Max. :4.5400 Max. :14.280 Max. :5.1000 Max. :42.81000
## our over remove internet
## Min. : 0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.0000
## 1st Qu.: 0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 0.0000
## Median : 0.0000 Median :0.0000 Median :0.0000 Median : 0.0000
## Mean : 0.3122 Mean :0.0959 Mean :0.1142 Mean : 0.1053
## 3rd Qu.: 0.3800 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 0.0000
## Max. :10.0000 Max. :5.8800 Max. :7.2700 Max. :11.1100
## order mail receive will
## Min. :0.00000 Min. : 0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.00000 Median : 0.0000 Median :0.00000 Median :0.1000
## Mean :0.09007 Mean : 0.2394 Mean :0.05982 Mean :0.5417
## 3rd Qu.:0.00000 3rd Qu.: 0.1600 3rd Qu.:0.00000 3rd Qu.:0.8000
## Max. :5.26000 Max. :18.1800 Max. :2.61000 Max. :9.6700
## people report addresses free
## Min. :0.00000 Min. : 0.00000 Min. :0.0000 Min. : 0.0000
## 1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.:0.0000 1st Qu.: 0.0000
## Median :0.00000 Median : 0.00000 Median :0.0000 Median : 0.0000
## Mean :0.09393 Mean : 0.05863 Mean :0.0492 Mean : 0.2488
## 3rd Qu.:0.00000 3rd Qu.: 0.00000 3rd Qu.:0.0000 3rd Qu.: 0.1000
## Max. :5.55000 Max. :10.00000 Max. :4.4100 Max. :20.0000
## business email you credit
## Min. :0.0000 Min. :0.0000 Min. : 0.000 Min. : 0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.: 0.00000
## Median :0.0000 Median :0.0000 Median : 1.310 Median : 0.00000
## Mean :0.1426 Mean :0.1847 Mean : 1.662 Mean : 0.08558
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 2.640 3rd Qu.: 0.00000
## Max. :7.1400 Max. :9.0900 Max. :18.750 Max. :18.18000
## your font num000 money
## Min. : 0.0000 Min. : 0.0000 Min. :0.0000 Min. : 0.00000
## 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:0.0000 1st Qu.: 0.00000
## Median : 0.2200 Median : 0.0000 Median :0.0000 Median : 0.00000
## Mean : 0.8098 Mean : 0.1212 Mean :0.1016 Mean : 0.09427
## 3rd Qu.: 1.2700 3rd Qu.: 0.0000 3rd Qu.:0.0000 3rd Qu.: 0.00000
## Max. :11.1100 Max. :17.1000 Max. :5.4500 Max. :12.50000
## hp hpl george num650
## Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. :0.0000
## 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:0.0000
## Median : 0.0000 Median : 0.0000 Median : 0.0000 Median :0.0000
## Mean : 0.5495 Mean : 0.2654 Mean : 0.7673 Mean :0.1248
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.:0.0000
## Max. :20.8300 Max. :16.6600 Max. :33.3300 Max. :9.0900
## lab labs telnet num857
## Min. : 0.00000 Min. :0.0000 Min. : 0.00000 Min. :0.00000
## 1st Qu.: 0.00000 1st Qu.:0.0000 1st Qu.: 0.00000 1st Qu.:0.00000
## Median : 0.00000 Median :0.0000 Median : 0.00000 Median :0.00000
## Mean : 0.09892 Mean :0.1029 Mean : 0.06475 Mean :0.04705
## 3rd Qu.: 0.00000 3rd Qu.:0.0000 3rd Qu.: 0.00000 3rd Qu.:0.00000
## Max. :14.28000 Max. :5.8800 Max. :12.50000 Max. :4.76000
## data num415 num85 technology
## Min. : 0.00000 Min. :0.00000 Min. : 0.0000 Min. :0.00000
## 1st Qu.: 0.00000 1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.:0.00000
## Median : 0.00000 Median :0.00000 Median : 0.0000 Median :0.00000
## Mean : 0.09723 Mean :0.04784 Mean : 0.1054 Mean :0.09748
## 3rd Qu.: 0.00000 3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.:0.00000
## Max. :18.18000 Max. :4.76000 Max. :20.0000 Max. :7.69000
## num1999 parts pm direct
## Min. :0.000 Min. :0.0000 Min. : 0.00000 Min. :0.00000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.: 0.00000 1st Qu.:0.00000
## Median :0.000 Median :0.0000 Median : 0.00000 Median :0.00000
## Mean :0.137 Mean :0.0132 Mean : 0.07863 Mean :0.06483
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.: 0.00000 3rd Qu.:0.00000
## Max. :6.890 Max. :8.3300 Max. :11.11000 Max. :4.76000
## cs meeting original project
## Min. :0.00000 Min. : 0.0000 Min. :0.0000 Min. : 0.0000
## 1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.:0.0000 1st Qu.: 0.0000
## Median :0.00000 Median : 0.0000 Median :0.0000 Median : 0.0000
## Mean :0.04367 Mean : 0.1323 Mean :0.0461 Mean : 0.0792
## 3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.:0.0000 3rd Qu.: 0.0000
## Max. :7.14000 Max. :14.2800 Max. :3.5700 Max. :20.0000
## re edu table conference
## Min. : 0.0000 Min. : 0.0000 Min. :0.000000 Min. : 0.00000
## 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:0.000000 1st Qu.: 0.00000
## Median : 0.0000 Median : 0.0000 Median :0.000000 Median : 0.00000
## Mean : 0.3012 Mean : 0.1798 Mean :0.005444 Mean : 0.03187
## 3rd Qu.: 0.1100 3rd Qu.: 0.0000 3rd Qu.:0.000000 3rd Qu.: 0.00000
## Max. :21.4200 Max. :22.0500 Max. :2.170000 Max. :10.00000
## charSemicolon charRoundbracket charSquarebracket charExclamation
## Min. :0.00000 Min. :0.000 Min. :0.00000 Min. : 0.0000
## 1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.: 0.0000
## Median :0.00000 Median :0.065 Median :0.00000 Median : 0.0000
## Mean :0.03857 Mean :0.139 Mean :0.01698 Mean : 0.2691
## 3rd Qu.:0.00000 3rd Qu.:0.188 3rd Qu.:0.00000 3rd Qu.: 0.3150
## Max. :4.38500 Max. :9.752 Max. :4.08100 Max. :32.4780
## charDollar charHash capitalAve capitalLong
## Min. :0.00000 Min. : 0.00000 Min. : 1.000 Min. : 1.00
## 1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.: 1.588 1st Qu.: 6.00
## Median :0.00000 Median : 0.00000 Median : 2.276 Median : 15.00
## Mean :0.07581 Mean : 0.04424 Mean : 5.191 Mean : 52.17
## 3rd Qu.:0.05200 3rd Qu.: 0.00000 3rd Qu.: 3.706 3rd Qu.: 43.00
## Max. :6.00300 Max. :19.82900 Max. :1102.500 Max. :9989.00
## capitalTotal type
## Min. : 1.0 nonspam:2788
## 1st Qu.: 35.0 spam :1813
## Median : 95.0
## Mean : 283.3
## 3rd Qu.: 266.0
## Max. :15841.0Creamos las particiones de los datos, utilizando 75% para training y el resto para test. Indicar cuantas observaciones tenemos para training y para test
inTrain<- createDataPartition(y=spam$type, p=0.75, list=FALSE)
training<-spam[inTrain,]
testing<-spam[-inTrain,]
dim(training)## [1] 3451 58dim(testing)## [1] 1150 58Creamos el modelo GLM utilizando todos las caracterásticas para clasificar el tipo de spam. Utilizamos los datos de training y revisamos los resultados obtenidos.
modelFit<-train(type ~.,data=training, method="glm")
modelFit$finalModel##
## Call: NULL
##
## Coefficients:
## (Intercept) make address
## -1.623e+00 -4.371e-01 -1.493e-01
## all num3d our
## 1.686e-01 1.752e+00 4.719e-01
## over remove internet
## 1.618e+00 2.061e+00 7.488e-01
## order mail receive
## 4.725e-01 1.352e-01 6.618e-02
## will people report
## -1.603e-01 -7.267e-02 1.538e-01
## addresses free business
## 8.789e-01 8.463e-01 1.182e+00
## email you credit
## 1.948e-01 7.988e-02 1.807e+00
## your font num000
## 2.099e-01 1.097e-01 2.883e+00
## money hp hpl
## 3.106e-01 -2.148e+00 -2.052e+00
## george num650 lab
## -1.984e+01 1.015e+00 -2.403e+00
## labs telnet num857
## -3.709e-01 9.656e-01 6.793e+00
## data num415 num85
## -5.179e-01 7.267e-01 -2.700e+00
## technology num1999 parts
## 7.471e-01 -9.550e-03 -5.615e-01
## pm direct cs
## -6.200e-01 -3.228e-01 -4.489e+01
## meeting original project
## -2.621e+00 -1.941e+00 -1.769e+00
## re edu table
## -7.522e-01 -1.732e+00 -2.173e+00
## conference charSemicolon charRoundbracket
## -3.833e+00 -1.170e+00 -1.980e-01
## charSquarebracket charExclamation charDollar
## -1.238e+00 5.362e-01 5.556e+00
## charHash capitalAve capitalLong
## 2.440e+00 3.824e-02 1.143e-02
## capitalTotal
## 5.728e-04
##
## Degrees of Freedom: 3450 Total (i.e. Null); 3393 Residual
## Null Deviance: 4628
## Residual Deviance: 1297 AIC: 1413modelFit## Generalized Linear Model
##
## 3451 samples
## 57 predictor
## 2 classes: 'nonspam', 'spam'
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 3451, 3451, 3451, 3451, 3451, 3451, ...
## Resampling results:
##
## Accuracy Kappa
## 0.9208353 0.8330403modelFit$results## parameter Accuracy Kappa AccuracySD KappaSD
## 1 none 0.9208353 0.8330403 0.008740484 0.01804277Utilizamos el modelo creado para predecir utilizando los datos de testing y dejarlo en “prediction”. Revisar los primeros datos en “prediction”
prediction<-predict(modelFit,newdata=testing)
head(prediction)## [1] spam spam spam spam spam spam
## Levels: nonspam spamconfusionMatrix(prediction,testing$type)## Confusion Matrix and Statistics
##
## Reference
## Prediction nonspam spam
## nonspam 654 41
## spam 43 412
##
## Accuracy : 0.927
## 95% CI : (0.9104, 0.9413)
## No Information Rate : 0.6061
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8471
## Mcnemar's Test P-Value : 0.9131
##
## Sensitivity : 0.9383
## Specificity : 0.9095
## Pos Pred Value : 0.9410
## Neg Pred Value : 0.9055
## Prevalence : 0.6061
## Detection Rate : 0.5687
## Detection Prevalence : 0.6043
## Balanced Accuracy : 0.9239
##
## 'Positive' Class : nonspam
##