The fireforest data set is being used for Support vector machine modelling by using the factors like wind ,rain, rh and temperature etc and predict the aize of burned area.
library(kernlab)## Warning: package 'kernlab' was built under R version 3.5.1library(caret)## Warning: package 'caret' was built under R version 3.5.1## Loading required package: lattice## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.5.1##
## Attaching package: 'ggplot2'## The following object is masked from 'package:kernlab':
##
## alphalibrary(plyr)## Warning: package 'plyr' was built under R version 3.5.1# Read the data
FF <- read.csv(file.choose())
View(FF)
class(FF)## [1] "data.frame"str(FF)## 'data.frame': 517 obs. of 31 variables:
## $ month : Factor w/ 12 levels "apr","aug","dec",..: 8 11 11 8 8 2 2 2 12 12 ...
## $ day : Factor w/ 7 levels "fri","mon","sat",..: 1 6 3 1 4 4 2 2 6 3 ...
## $ FFMC : num 86.2 90.6 90.6 91.7 89.3 92.3 92.3 91.5 91 92.5 ...
## $ DMC : num 26.2 35.4 43.7 33.3 51.3 ...
## $ DC : num 94.3 669.1 686.9 77.5 102.2 ...
## $ ISI : num 5.1 6.7 6.7 9 9.6 14.7 8.5 10.7 7 7.1 ...
## $ temp : num 8.2 18 14.6 8.3 11.4 22.2 24.1 8 13.1 22.8 ...
## $ RH : int 51 33 33 97 99 29 27 86 63 40 ...
## $ wind : num 6.7 0.9 1.3 4 1.8 5.4 3.1 2.2 5.4 4 ...
## $ rain : num 0 0 0 0.2 0 0 0 0 0 0 ...
## $ area : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dayfri : int 1 0 0 1 0 0 0 0 0 0 ...
## $ daymon : int 0 0 0 0 0 0 1 1 0 0 ...
## $ daysat : int 0 0 1 0 0 0 0 0 0 1 ...
## $ daysun : int 0 0 0 0 1 1 0 0 0 0 ...
## $ daythu : int 0 0 0 0 0 0 0 0 0 0 ...
## $ daytue : int 0 1 0 0 0 0 0 0 1 0 ...
## $ daywed : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthapr : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthaug : int 0 0 0 0 0 1 1 1 0 0 ...
## $ monthdec : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthfeb : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthjan : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthjul : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthjun : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthmar : int 1 0 0 1 1 0 0 0 0 0 ...
## $ monthmay : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthnov : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthoct : int 0 1 1 0 0 0 0 0 0 0 ...
## $ monthsep : int 0 0 0 0 0 0 0 0 1 1 ...
## $ size_category: Factor w/ 2 levels "large","small": 2 2 2 2 2 2 2 2 2 2 ...# The area value has lots of zeros
hist(FF$area)
rug(FF$area)
# Transform the Area value to Y
FF1 <- mutate(FF, y = log(area + 1)) # default is to the base e, y is lower case
hist(FF1$y)
summary(FF) # Confirms on the different scale and demands normalizing the data.## month day FFMC DMC DC
## aug :184 fri:85 Min. :18.70 Min. : 1.1 Min. : 7.9
## sep :172 mon:74 1st Qu.:90.20 1st Qu.: 68.6 1st Qu.:437.7
## mar : 54 sat:84 Median :91.60 Median :108.3 Median :664.2
## jul : 32 sun:95 Mean :90.64 Mean :110.9 Mean :547.9
## feb : 20 thu:61 3rd Qu.:92.90 3rd Qu.:142.4 3rd Qu.:713.9
## jun : 17 tue:64 Max. :96.20 Max. :291.3 Max. :860.6
## (Other): 38 wed:54
## ISI temp RH wind
## Min. : 0.000 Min. : 2.20 Min. : 15.00 Min. :0.400
## 1st Qu.: 6.500 1st Qu.:15.50 1st Qu.: 33.00 1st Qu.:2.700
## Median : 8.400 Median :19.30 Median : 42.00 Median :4.000
## Mean : 9.022 Mean :18.89 Mean : 44.29 Mean :4.018
## 3rd Qu.:10.800 3rd Qu.:22.80 3rd Qu.: 53.00 3rd Qu.:4.900
## Max. :56.100 Max. :33.30 Max. :100.00 Max. :9.400
##
## rain area dayfri daymon
## Min. :0.00000 Min. : 0.00 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.: 0.00 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median : 0.52 Median :0.0000 Median :0.0000
## Mean :0.02166 Mean : 12.85 Mean :0.1644 Mean :0.1431
## 3rd Qu.:0.00000 3rd Qu.: 6.57 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :6.40000 Max. :1090.84 Max. :1.0000 Max. :1.0000
##
## daysat daysun daythu daytue
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.1625 Mean :0.1838 Mean :0.118 Mean :0.1238
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
##
## daywed monthapr monthaug monthdec
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.1044 Mean :0.01741 Mean :0.3559 Mean :0.01741
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.00000
##
## monthfeb monthjan monthjul monthjun
## Min. :0.00000 Min. :0.000000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.00000 Median :0.000000 Median :0.0000 Median :0.00000
## Mean :0.03868 Mean :0.003868 Mean :0.0619 Mean :0.03288
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.000000 Max. :1.0000 Max. :1.00000
##
## monthmar monthmay monthnov monthoct
## Min. :0.0000 Min. :0.000000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.0000 Median :0.000000 Median :0.000000 Median :0.00000
## Mean :0.1044 Mean :0.003868 Mean :0.001934 Mean :0.02901
## 3rd Qu.:0.0000 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.000000 Max. :1.000000 Max. :1.00000
##
## monthsep size_category
## Min. :0.0000 large:139
## 1st Qu.:0.0000 small:378
## Median :0.0000
## Mean :0.3327
## 3rd Qu.:1.0000
## Max. :1.0000
## # Prediction of Forest fires requires only prediction from
# temperature, rain, relative humidity and wind speed
# Apply Normalization technique to the whole dataset :
normalize<-function(x){
return ( (x-min(x))/(max(x)-min(x)))
}
FF$temp = normalize(FF$temp)
FF$RH = normalize(FF$RH)
FF$wind = normalize(FF$wind)
FF$rain = normalize(FF$rain)
# We need to tweak this as a classification problem.lets base out the Size using this criteria :
attach(FF)
# Data Partition
set.seed(123)
ind <- sample(2, nrow(FF), replace = TRUE, prob = c(0.7,0.3))
FF_train <- FF[ind==1,]
FF_test <- FF[ind==2,]
# to train model
# e1071 package from LIBSVM library
# SVMlight algorithm klar package
# kvsm() function uses gaussian RBF kernel
# Building model
model1<-ksvm(size_category~temp+rain+wind+RH,
data= FF_train,kernel = "vanilladot")## Setting default kernel parametersmodel1## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 1
##
## Linear (vanilla) kernel function.
##
## Number of Support Vectors : 199
##
## Objective Function Value : -184
## Training error : 0.247978Area_pred <- predict(model1, FF_test)
table(Area_pred,FF_test$size_category)##
## Area_pred large small
## large 0 0
## small 47 99agreement <- Area_pred == FF_test$size_category
table(agreement)## agreement
## FALSE TRUE
## 47 99prop.table(table(agreement))## agreement
## FALSE TRUE
## 0.3219178 0.6780822# Different types of kernels
# "rbfdot", "polydot", "tanhdot", "vanilladot", "laplacedot",
# "besseldot", "anovadot", "splinedot", "matrix"
# kernel = rfdot
model_rfdot<-ksvm(size_category~temp+rain+wind+RH,
data= FF_train,kernel = "rbfdot")
pred_rfdot<-predict(model_rfdot,newdata=FF_test)
mean(pred_rfdot==FF_test$size_category) # 68.41## [1] 0.6849315# kernel = vanilladot
model_vanilla<-ksvm(size_category~temp+rain+wind+RH,
data= FF_train,kernel = "vanilladot")## Setting default kernel parameterspred_vanilla<-predict(model_vanilla,newdata=FF_test)
mean(pred_vanilla==FF_test$size_category) # 67.80## [1] 0.6780822# kernal = besseldot
model_besseldot<-ksvm(size_category~temp+rain+wind+RH,
data= FF_train,kernel = "besseldot")## Setting default kernel parameterspred_bessel<-predict(model_besseldot,newdata=FF_test)
mean(pred_bessel==FF_test$size_category) # 67.80## [1] 0.6780822# kernel = polydot
model_poly<-ksvm(size_category~temp+rain+wind+RH,
data= FF_train,kernel = "polydot")## Setting default kernel parameterspred_poly<-predict(model_poly,newdata = FF_test)
mean(pred_poly==FF_test$size_category) # 67.80## [1] 0.6780822