df_fundraising <- readRDS("fundraising.rds")
head(df_fundraising)
## # A tibble: 6 x 21
## zipconvert2 zipconvert3 zipconvert4 zipconvert5 homeowner num_child income
## <fct> <fct> <fct> <fct> <fct> <dbl> <dbl>
## 1 Yes No No No Yes 1 1
## 2 No No No Yes No 2 5
## 3 No No No Yes Yes 1 3
## 4 No Yes No No Yes 1 4
## 5 No Yes No No Yes 1 4
## 6 No No No Yes Yes 1 4
## # ... with 14 more variables: female <fct>, wealth <dbl>, home_value <dbl>,
## # med_fam_inc <dbl>, avg_fam_inc <dbl>, pct_lt15k <dbl>, num_prom <dbl>,
## # lifetime_gifts <dbl>, largest_gift <dbl>, last_gift <dbl>,
## # months_since_donate <dbl>, time_lag <dbl>, avg_gift <dbl>, target <fct>
#understanding Donor and non-donors'
#Convert the data to a matrix and a vector for the response
set.seed(12)
x = model.matrix(target~.,df_fundraising)[,-1]
y = as.factor(df_fundraising$target)
#split the data into training and validation sets(70- 30 split)
fundtrain = sample(1:nrow(x), nrow(x)/1.43) #Row indexes
fundtest = (-fundtrain)#Row indexes for test set
ytest = y[fundtest]
table(df_fundraising[fundtrain,]$target)
##
## Donor No Donor
## 1038 1059
table(df_fundraising[fundtest,]$target)
##
## Donor No Donor
## 461 442
#selecting lambda using 10-fold cross validation
set.seed(12)
grid=10^seq(10,-2,length=100)
lasso_cv <- cv.glmnet(x[fundtrain,],y[fundtrain],alpha=1,lambda = grid,standized = TRUE,tresh=1e-12,family = "binomial" )
plot(lasso_cv)
lasso_best=lasso_cv$lambda.min
#predicting the MSE using the best lambda
lasso_pred=predict(lasso_cv,s=lasso_best,newx=x[fundtest,])
lasso_MSE <- mean((lasso_pred -ytest)^2)
## Warning in Ops.factor(lasso_pred, ytest): '-' not meaningful for factors
lasso_MSE
## [1] NA
#getting the coefficients which are non zero
lasso_best=glmnet(x[fundtrain,],y[fundtrain],alpha=1,lambda=lasso_cv$lambda.min,family = "binomial")
lasso_best=glmnet(x,y,alpha=1,lambda=lasso_cv$lambda.min,family = "binomial")
lasso_coef=predict(lasso_best,type="coefficients",s=lasso_cv$lambda.min)[1:21,]
length(lasso_coef[lasso_coef!=0])
## [1] 7
# length(lasso_coef[lasso_coef==0])
lasso_coef
## (Intercept) zipconvert2Yes zipconvert3No zipconvert4Yes
## -1.588867746 0.000000000 0.000000000 0.000000000
## zipconvert5Yes homeownerNo num_child income
## 0.000000000 0.000000000 0.107080722 -0.029404872
## femaleNo wealth home_value med_fam_inc
## 0.000000000 0.000000000 0.000000000 0.000000000
## avg_fam_inc pct_lt15k num_prom lifetime_gifts
## 0.000000000 0.000000000 -0.001033417 0.000000000
## largest_gift last_gift months_since_donate time_lag
## 0.000000000 0.005658545 0.049596561 0.000000000
## avg_gift
## 0.001897456
target_preds <- predict(lasso_best,type="response",s=lasso_cv$lambda.min,newx=x[fundtest,])
predicted.target <- ifelse(target_preds > 0.5, "Donor", "No Donor")
mean(mean(predicted.target == y[fundtest]))
## [1] 0.4496124
#Split data into train and test
# Recode factor levels by name
levels(df_fundraising$target) <- list("Donor" = "Yes", 'No Donor' = "No")
df_fundraising$target <- recode_factor(df_fundraising$target, Donor = "Yes",
'No Donor' = "No")
index <- createDataPartition(df_fundraising$target, p=0.75, list=FALSE)
trainSet <- df_fundraising[index,]
## Warning: The `i` argument of ``[`()` can't be a matrix as of tibble 3.0.0.
## Convert to a vector.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
testSet <- df_fundraising[-index,]
#Defining the training controls for multiple models
fitControl <- trainControl(
method = "cv",
number = 10,
savePredictions = 'final',
classProbs = TRUE
)
#Defining the predictors and outcome using the LASSO predictors
predictors<-c("num_child","income","num_prom","last_gift","months_since_donate","avg_gift")
# outcomeName<-c('target')
str(trainSet)
## tibble [2,251 x 21] (S3: tbl_df/tbl/data.frame)
## $ zipconvert2 : Factor w/ 2 levels "No","Yes": 2 1 1 1 2 1 2 1 1 1 ...
## $ zipconvert3 : Factor w/ 2 levels "Yes","No": 2 2 1 1 2 2 2 2 2 2 ...
## $ zipconvert4 : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 1 1 2 1 ...
## $ zipconvert5 : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 2 1 2 ...
## $ homeowner : Factor w/ 2 levels "Yes","No": 1 1 1 1 1 1 2 2 1 1 ...
## $ num_child : num [1:2251] 1 1 1 1 1 1 1 1 1 1 ...
## $ income : num [1:2251] 1 3 4 4 4 4 5 2 3 4 ...
## $ female : Factor w/ 2 levels "Yes","No": 2 2 2 1 1 1 2 2 1 2 ...
## $ wealth : num [1:2251] 7 4 8 8 8 5 8 8 5 6 ...
## $ home_value : num [1:2251] 698 1471 547 482 1438 ...
## $ med_fam_inc : num [1:2251] 422 484 386 242 458 434 399 337 434 354 ...
## $ avg_fam_inc : num [1:2251] 463 546 432 275 533 472 458 402 500 393 ...
## $ pct_lt15k : num [1:2251] 4 4 7 28 8 6 8 5 3 13 ...
## $ num_prom : num [1:2251] 46 94 20 38 21 59 25 27 98 60 ...
## $ lifetime_gifts : num [1:2251] 94 177 23 73 26 84 40 50 126 100 ...
## $ largest_gift : num [1:2251] 12 10 11 10 16 5 10 20 7 10 ...
## $ last_gift : num [1:2251] 12 8 11 10 16 3 10 20 7 10 ...
## $ months_since_donate: num [1:2251] 34 30 30 31 30 30 32 37 33 30 ...
## $ time_lag : num [1:2251] 6 3 6 3 6 12 2 7 1 10 ...
## $ avg_gift : num [1:2251] 9.4 7.08 7.67 7.3 13 ...
## $ target : Factor w/ 2 levels "Yes","No": 1 2 2 1 2 2 1 2 1 1 ...
#Training the random forest model
model_rf<-train(trainSet[,predictors],trainSet$target,method='rf',trControl=fitControl,tuneLength=3)
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#Predicting using random forest model
testSet$pred_rf<-predict(object = model_rf,testSet[,predictors])
#Checking the accuracy of the random forest model
confusionMatrix(testSet$target,testSet$pred_rf)
## Confusion Matrix and Statistics
##
## Reference
## Prediction Yes No
## Yes 202 172
## No 169 206
##
## Accuracy : 0.5447
## 95% CI : (0.5083, 0.5808)
## No Information Rate : 0.5047
## P-Value [Acc > NIR] : 0.0155
##
## Kappa : 0.0894
##
## Mcnemar's Test P-Value : 0.9138
##
## Sensitivity : 0.5445
## Specificity : 0.5450
## Pos Pred Value : 0.5401
## Neg Pred Value : 0.5493
## Prevalence : 0.4953
## Detection Rate : 0.2697
## Detection Prevalence : 0.4993
## Balanced Accuracy : 0.5447
##
## 'Positive' Class : Yes
##
#Training the KNN model
model_knn<-train(trainSet[,predictors],trainSet$target,method='knn',trControl=fitControl,tuneLength=5)
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#Predicting using KNN model
testSet$pred_knn<-predict(object = model_knn,testSet[,predictors])
#Checking the accuracy
confusionMatrix(testSet$target,testSet$pred_knn)
## Confusion Matrix and Statistics
##
## Reference
## Prediction Yes No
## Yes 201 173
## No 168 207
##
## Accuracy : 0.5447
## 95% CI : (0.5083, 0.5808)
## No Information Rate : 0.5073
## P-Value [Acc > NIR] : 0.02216
##
## Kappa : 0.0894
##
## Mcnemar's Test P-Value : 0.82851
##
## Sensitivity : 0.5447
## Specificity : 0.5447
## Pos Pred Value : 0.5374
## Neg Pred Value : 0.5520
## Prevalence : 0.4927
## Detection Rate : 0.2684
## Detection Prevalence : 0.4993
## Balanced Accuracy : 0.5447
##
## 'Positive' Class : Yes
##
#Training the logistic regression model
model_lr<-train(trainSet[,predictors],trainSet$target,method='glm',trControl=fitControl,tuneLength=3)
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#Predicting using logistic regression model
testSet$pred_lr<-predict(object = model_lr,testSet[,predictors])
#Checking the accuracy
confusionMatrix(testSet$target,testSet$pred_lr)
## Confusion Matrix and Statistics
##
## Reference
## Prediction Yes No
## Yes 216 158
## No 177 198
##
## Accuracy : 0.5527
## 95% CI : (0.5163, 0.5887)
## No Information Rate : 0.5247
## P-Value [Acc > NIR] : 0.06669
##
## Kappa : 0.1055
##
## Mcnemar's Test P-Value : 0.32539
##
## Sensitivity : 0.5496
## Specificity : 0.5562
## Pos Pred Value : 0.5775
## Neg Pred Value : 0.5280
## Prevalence : 0.5247
## Detection Rate : 0.2884
## Detection Prevalence : 0.4993
## Balanced Accuracy : 0.5529
##
## 'Positive' Class : Yes
##
#Training the GBM
model_gbm<-train(trainSet[,predictors],trainSet$target,method='gbm',trControl=fitControl,tuneLength=3)
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## 80 1.2843 nan 0.1000 -0.0007
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## 120 1.2669 nan 0.1000 -0.0008
## 140 1.2578 nan 0.1000 -0.0003
## 150 1.2534 nan 0.1000 -0.0010
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3833 nan 0.1000 0.0009
## 2 1.3809 nan 0.1000 0.0010
## 3 1.3788 nan 0.1000 0.0007
## 4 1.3768 nan 0.1000 0.0006
## 5 1.3749 nan 0.1000 0.0001
## 6 1.3728 nan 0.1000 0.0007
## 7 1.3712 nan 0.1000 0.0006
## 8 1.3690 nan 0.1000 0.0005
## 9 1.3679 nan 0.1000 0.0002
## 10 1.3664 nan 0.1000 0.0005
## 20 1.3562 nan 0.1000 0.0002
## 40 1.3441 nan 0.1000 -0.0004
## 60 1.3375 nan 0.1000 -0.0002
## 80 1.3322 nan 0.1000 -0.0004
## 100 1.3290 nan 0.1000 -0.0003
## 120 1.3264 nan 0.1000 -0.0002
## 140 1.3239 nan 0.1000 -0.0004
## 150 1.3232 nan 0.1000 -0.0004
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3829 nan 0.1000 0.0004
## 2 1.3785 nan 0.1000 0.0014
## 3 1.3746 nan 0.1000 0.0017
## 4 1.3717 nan 0.1000 0.0010
## 5 1.3688 nan 0.1000 0.0006
## 6 1.3657 nan 0.1000 0.0006
## 7 1.3630 nan 0.1000 0.0006
## 8 1.3613 nan 0.1000 -0.0001
## 9 1.3586 nan 0.1000 0.0006
## 10 1.3572 nan 0.1000 -0.0008
## 20 1.3409 nan 0.1000 -0.0000
## 40 1.3276 nan 0.1000 -0.0007
## 60 1.3160 nan 0.1000 0.0001
## 80 1.3068 nan 0.1000 -0.0008
## 100 1.2998 nan 0.1000 -0.0003
## 120 1.2923 nan 0.1000 -0.0002
## 140 1.2859 nan 0.1000 -0.0007
## 150 1.2823 nan 0.1000 -0.0006
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3812 nan 0.1000 0.0015
## 2 1.3753 nan 0.1000 0.0017
## 3 1.3713 nan 0.1000 0.0008
## 4 1.3677 nan 0.1000 0.0006
## 5 1.3644 nan 0.1000 0.0009
## 6 1.3608 nan 0.1000 0.0009
## 7 1.3581 nan 0.1000 0.0009
## 8 1.3551 nan 0.1000 -0.0001
## 9 1.3528 nan 0.1000 -0.0004
## 10 1.3499 nan 0.1000 0.0001
## 20 1.3322 nan 0.1000 -0.0009
## 40 1.3112 nan 0.1000 -0.0005
## 60 1.2976 nan 0.1000 -0.0009
## 80 1.2847 nan 0.1000 -0.0005
## 100 1.2724 nan 0.1000 -0.0007
## 120 1.2651 nan 0.1000 -0.0010
## 140 1.2567 nan 0.1000 -0.0011
## 150 1.2504 nan 0.1000 -0.0004
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3835 nan 0.1000 0.0008
## 2 1.3809 nan 0.1000 0.0005
## 3 1.3788 nan 0.1000 0.0007
## 4 1.3762 nan 0.1000 0.0009
## 5 1.3745 nan 0.1000 0.0007
## 6 1.3726 nan 0.1000 0.0006
## 7 1.3707 nan 0.1000 0.0003
## 8 1.3694 nan 0.1000 0.0004
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## 10 1.3667 nan 0.1000 0.0002
## 20 1.3552 nan 0.1000 -0.0002
## 40 1.3427 nan 0.1000 -0.0005
## 60 1.3353 nan 0.1000 -0.0003
## 80 1.3315 nan 0.1000 -0.0005
## 100 1.3282 nan 0.1000 -0.0002
## 120 1.3268 nan 0.1000 -0.0003
## 140 1.3246 nan 0.1000 -0.0002
## 150 1.3240 nan 0.1000 -0.0008
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3827 nan 0.1000 0.0012
## 2 1.3781 nan 0.1000 0.0013
## 3 1.3751 nan 0.1000 0.0012
## 4 1.3717 nan 0.1000 0.0009
## 5 1.3680 nan 0.1000 0.0009
## 6 1.3658 nan 0.1000 0.0001
## 7 1.3625 nan 0.1000 0.0002
## 8 1.3604 nan 0.1000 0.0002
## 9 1.3579 nan 0.1000 0.0006
## 10 1.3562 nan 0.1000 -0.0004
## 20 1.3438 nan 0.1000 -0.0001
## 40 1.3287 nan 0.1000 -0.0008
## 60 1.3196 nan 0.1000 -0.0004
## 80 1.3123 nan 0.1000 -0.0006
## 100 1.3059 nan 0.1000 -0.0005
## 120 1.2996 nan 0.1000 -0.0008
## 140 1.2951 nan 0.1000 -0.0007
## 150 1.2925 nan 0.1000 -0.0007
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3813 nan 0.1000 0.0010
## 2 1.3763 nan 0.1000 0.0022
## 3 1.3730 nan 0.1000 0.0007
## 4 1.3698 nan 0.1000 0.0004
## 5 1.3657 nan 0.1000 0.0011
## 6 1.3621 nan 0.1000 0.0009
## 7 1.3596 nan 0.1000 0.0008
## 8 1.3574 nan 0.1000 -0.0001
## 9 1.3550 nan 0.1000 0.0003
## 10 1.3528 nan 0.1000 0.0001
## 20 1.3348 nan 0.1000 -0.0006
## 40 1.3144 nan 0.1000 -0.0008
## 60 1.3036 nan 0.1000 -0.0001
## 80 1.2924 nan 0.1000 -0.0005
## 100 1.2809 nan 0.1000 -0.0011
## 120 1.2716 nan 0.1000 -0.0005
## 140 1.2607 nan 0.1000 -0.0009
## 150 1.2569 nan 0.1000 -0.0007
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3834 nan 0.1000 0.0006
## 2 1.3805 nan 0.1000 0.0014
## 3 1.3782 nan 0.1000 0.0009
## 4 1.3761 nan 0.1000 0.0005
## 5 1.3736 nan 0.1000 0.0008
## 6 1.3710 nan 0.1000 0.0009
## 7 1.3689 nan 0.1000 0.0007
## 8 1.3673 nan 0.1000 0.0004
## 9 1.3659 nan 0.1000 0.0004
## 10 1.3649 nan 0.1000 -0.0001
## 20 1.3538 nan 0.1000 -0.0003
## 40 1.3411 nan 0.1000 -0.0001
## 60 1.3338 nan 0.1000 -0.0000
## 80 1.3293 nan 0.1000 -0.0003
## 100 1.3259 nan 0.1000 -0.0003
## 120 1.3234 nan 0.1000 -0.0005
## 140 1.3212 nan 0.1000 -0.0004
## 150 1.3199 nan 0.1000 -0.0002
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3814 nan 0.1000 0.0012
## 2 1.3772 nan 0.1000 0.0020
## 3 1.3742 nan 0.1000 0.0007
## 4 1.3702 nan 0.1000 0.0011
## 5 1.3662 nan 0.1000 0.0015
## 6 1.3644 nan 0.1000 -0.0001
## 7 1.3611 nan 0.1000 0.0005
## 8 1.3586 nan 0.1000 0.0006
## 9 1.3565 nan 0.1000 0.0006
## 10 1.3542 nan 0.1000 0.0005
## 20 1.3407 nan 0.1000 -0.0005
## 40 1.3238 nan 0.1000 -0.0000
## 60 1.3135 nan 0.1000 -0.0008
## 80 1.3061 nan 0.1000 -0.0008
## 100 1.2970 nan 0.1000 -0.0004
## 120 1.2906 nan 0.1000 -0.0004
## 140 1.2849 nan 0.1000 -0.0005
## 150 1.2828 nan 0.1000 -0.0004
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3794 nan 0.1000 0.0021
## 2 1.3744 nan 0.1000 0.0015
## 3 1.3693 nan 0.1000 0.0017
## 4 1.3647 nan 0.1000 0.0010
## 5 1.3615 nan 0.1000 0.0008
## 6 1.3588 nan 0.1000 0.0005
## 7 1.3554 nan 0.1000 0.0006
## 8 1.3526 nan 0.1000 0.0005
## 9 1.3495 nan 0.1000 0.0003
## 10 1.3475 nan 0.1000 -0.0002
## 20 1.3329 nan 0.1000 -0.0003
## 40 1.3116 nan 0.1000 -0.0008
## 60 1.2953 nan 0.1000 -0.0007
## 80 1.2837 nan 0.1000 -0.0001
## 100 1.2725 nan 0.1000 -0.0004
## 120 1.2619 nan 0.1000 -0.0013
## 140 1.2543 nan 0.1000 -0.0006
## 150 1.2493 nan 0.1000 -0.0012
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3833 nan 0.1000 0.0010
## 2 1.3810 nan 0.1000 0.0011
## 3 1.3783 nan 0.1000 0.0007
## 4 1.3759 nan 0.1000 0.0009
## 5 1.3735 nan 0.1000 0.0011
## 6 1.3713 nan 0.1000 0.0007
## 7 1.3698 nan 0.1000 0.0004
## 8 1.3676 nan 0.1000 0.0006
## 9 1.3665 nan 0.1000 -0.0002
## 10 1.3647 nan 0.1000 0.0006
## 20 1.3521 nan 0.1000 0.0003
## 40 1.3399 nan 0.1000 0.0001
## 60 1.3337 nan 0.1000 -0.0004
## 80 1.3301 nan 0.1000 -0.0002
## 100 1.3272 nan 0.1000 -0.0001
## 120 1.3249 nan 0.1000 -0.0002
## 140 1.3227 nan 0.1000 -0.0004
## 150 1.3214 nan 0.1000 -0.0002
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3819 nan 0.1000 0.0011
## 2 1.3770 nan 0.1000 0.0017
## 3 1.3732 nan 0.1000 0.0007
## 4 1.3688 nan 0.1000 0.0008
## 5 1.3652 nan 0.1000 0.0009
## 6 1.3628 nan 0.1000 0.0006
## 7 1.3615 nan 0.1000 0.0000
## 8 1.3592 nan 0.1000 0.0008
## 9 1.3572 nan 0.1000 0.0005
## 10 1.3559 nan 0.1000 -0.0000
## 20 1.3407 nan 0.1000 -0.0004
## 40 1.3261 nan 0.1000 -0.0001
## 60 1.3158 nan 0.1000 -0.0005
## 80 1.3072 nan 0.1000 -0.0003
## 100 1.3001 nan 0.1000 -0.0009
## 120 1.2916 nan 0.1000 -0.0004
## 140 1.2858 nan 0.1000 -0.0008
## 150 1.2828 nan 0.1000 -0.0006
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3817 nan 0.1000 0.0018
## 2 1.3757 nan 0.1000 0.0028
## 3 1.3719 nan 0.1000 0.0016
## 4 1.3673 nan 0.1000 0.0009
## 5 1.3640 nan 0.1000 0.0006
## 6 1.3601 nan 0.1000 0.0007
## 7 1.3571 nan 0.1000 0.0005
## 8 1.3544 nan 0.1000 0.0011
## 9 1.3519 nan 0.1000 -0.0001
## 10 1.3500 nan 0.1000 -0.0003
## 20 1.3320 nan 0.1000 -0.0000
## 40 1.3129 nan 0.1000 -0.0001
## 60 1.3001 nan 0.1000 -0.0004
## 80 1.2877 nan 0.1000 -0.0002
## 100 1.2749 nan 0.1000 -0.0003
## 120 1.2638 nan 0.1000 -0.0003
## 140 1.2541 nan 0.1000 -0.0008
## 150 1.2491 nan 0.1000 -0.0011
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3834 nan 0.1000 0.0010
## 2 1.3811 nan 0.1000 0.0005
## 3 1.3778 nan 0.1000 0.0010
## 4 1.3753 nan 0.1000 0.0009
## 5 1.3733 nan 0.1000 0.0009
## 6 1.3709 nan 0.1000 0.0007
## 7 1.3686 nan 0.1000 -0.0001
## 8 1.3667 nan 0.1000 0.0004
## 9 1.3654 nan 0.1000 0.0002
## 10 1.3642 nan 0.1000 0.0001
## 20 1.3524 nan 0.1000 -0.0003
## 40 1.3408 nan 0.1000 -0.0005
## 60 1.3335 nan 0.1000 -0.0004
## 80 1.3288 nan 0.1000 -0.0005
## 100 1.3257 nan 0.1000 -0.0003
## 120 1.3225 nan 0.1000 -0.0003
## 140 1.3204 nan 0.1000 -0.0004
## 150 1.3193 nan 0.1000 -0.0003
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3813 nan 0.1000 0.0011
## 2 1.3762 nan 0.1000 0.0014
## 3 1.3716 nan 0.1000 0.0009
## 4 1.3686 nan 0.1000 0.0008
## 5 1.3648 nan 0.1000 0.0010
## 6 1.3627 nan 0.1000 0.0003
## 7 1.3612 nan 0.1000 0.0002
## 8 1.3595 nan 0.1000 0.0004
## 9 1.3569 nan 0.1000 0.0009
## 10 1.3551 nan 0.1000 0.0005
## 20 1.3402 nan 0.1000 0.0001
## 40 1.3248 nan 0.1000 -0.0003
## 60 1.3136 nan 0.1000 -0.0008
## 80 1.3068 nan 0.1000 -0.0000
## 100 1.3008 nan 0.1000 -0.0002
## 120 1.2929 nan 0.1000 -0.0007
## 140 1.2862 nan 0.1000 -0.0005
## 150 1.2836 nan 0.1000 -0.0006
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3821 nan 0.1000 0.0009
## 2 1.3766 nan 0.1000 0.0020
## 3 1.3720 nan 0.1000 0.0011
## 4 1.3679 nan 0.1000 0.0009
## 5 1.3639 nan 0.1000 0.0006
## 6 1.3601 nan 0.1000 0.0010
## 7 1.3579 nan 0.1000 0.0004
## 8 1.3556 nan 0.1000 0.0002
## 9 1.3532 nan 0.1000 -0.0003
## 10 1.3514 nan 0.1000 -0.0004
## 20 1.3318 nan 0.1000 -0.0003
## 40 1.3127 nan 0.1000 -0.0008
## 60 1.3001 nan 0.1000 -0.0010
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## 100 1.2768 nan 0.1000 -0.0005
## 120 1.2662 nan 0.1000 -0.0009
## 140 1.2549 nan 0.1000 -0.0007
## 150 1.2498 nan 0.1000 -0.0007
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3838 nan 0.1000 0.0010
## 2 1.3811 nan 0.1000 0.0008
## 3 1.3795 nan 0.1000 0.0007
## 4 1.3772 nan 0.1000 0.0005
## 5 1.3747 nan 0.1000 0.0007
## 6 1.3726 nan 0.1000 0.0007
## 7 1.3710 nan 0.1000 0.0003
## 8 1.3689 nan 0.1000 0.0007
## 9 1.3671 nan 0.1000 0.0002
## 10 1.3654 nan 0.1000 0.0003
## 20 1.3536 nan 0.1000 0.0001
## 40 1.3428 nan 0.1000 -0.0001
## 60 1.3359 nan 0.1000 -0.0004
## 80 1.3311 nan 0.1000 -0.0003
## 100 1.3274 nan 0.1000 -0.0006
## 120 1.3253 nan 0.1000 -0.0003
## 140 1.3227 nan 0.1000 -0.0000
## 150 1.3221 nan 0.1000 -0.0005
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3808 nan 0.1000 0.0019
## 2 1.3775 nan 0.1000 0.0013
## 3 1.3739 nan 0.1000 0.0008
## 4 1.3701 nan 0.1000 0.0016
## 5 1.3672 nan 0.1000 0.0001
## 6 1.3646 nan 0.1000 0.0010
## 7 1.3627 nan 0.1000 -0.0002
## 8 1.3614 nan 0.1000 0.0000
## 9 1.3585 nan 0.1000 0.0007
## 10 1.3564 nan 0.1000 0.0004
## 20 1.3426 nan 0.1000 0.0001
## 40 1.3270 nan 0.1000 -0.0003
## 60 1.3142 nan 0.1000 -0.0007
## 80 1.3068 nan 0.1000 -0.0004
## 100 1.3005 nan 0.1000 -0.0007
## 120 1.2951 nan 0.1000 -0.0004
## 140 1.2900 nan 0.1000 -0.0004
## 150 1.2868 nan 0.1000 -0.0011
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3818 nan 0.1000 0.0016
## 2 1.3777 nan 0.1000 0.0014
## 3 1.3717 nan 0.1000 0.0017
## 4 1.3683 nan 0.1000 0.0002
## 5 1.3651 nan 0.1000 0.0008
## 6 1.3611 nan 0.1000 0.0013
## 7 1.3582 nan 0.1000 0.0007
## 8 1.3549 nan 0.1000 0.0009
## 9 1.3522 nan 0.1000 0.0004
## 10 1.3500 nan 0.1000 0.0001
## 20 1.3316 nan 0.1000 0.0001
## 40 1.3122 nan 0.1000 -0.0007
## 60 1.2954 nan 0.1000 -0.0003
## 80 1.2841 nan 0.1000 0.0001
## 100 1.2738 nan 0.1000 -0.0012
## 120 1.2640 nan 0.1000 -0.0002
## 140 1.2542 nan 0.1000 -0.0004
## 150 1.2489 nan 0.1000 -0.0007
## Warning: Setting row names on a tibble is deprecated.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3815 nan 0.1000 0.0019
## 2 1.3770 nan 0.1000 0.0012
## 3 1.3738 nan 0.1000 0.0010
## 4 1.3709 nan 0.1000 0.0008
## 5 1.3681 nan 0.1000 0.0006
## 6 1.3656 nan 0.1000 0.0003
## 7 1.3630 nan 0.1000 0.0010
## 8 1.3610 nan 0.1000 0.0001
## 9 1.3582 nan 0.1000 0.0004
## 10 1.3560 nan 0.1000 0.0006
## 20 1.3428 nan 0.1000 -0.0003
## 40 1.3295 nan 0.1000 -0.0004
## 60 1.3201 nan 0.1000 -0.0004
## 80 1.3123 nan 0.1000 -0.0007
## 100 1.3051 nan 0.1000 -0.0007
## 120 1.2986 nan 0.1000 -0.0003
## 140 1.2939 nan 0.1000 -0.0005
## 150 1.2917 nan 0.1000 -0.0007
#Predicting
testSet$pred_gbm<-predict(object = model_gbm,testSet[,predictors])
#Checking the accuracy
confusionMatrix(testSet$target,testSet$pred_gbm)
## Confusion Matrix and Statistics
##
## Reference
## Prediction Yes No
## Yes 215 159
## No 188 187
##
## Accuracy : 0.5367
## 95% CI : (0.5003, 0.5729)
## No Information Rate : 0.5381
## P-Value [Acc > NIR] : 0.5441
##
## Kappa : 0.0735
##
## Mcnemar's Test P-Value : 0.1328
##
## Sensitivity : 0.5335
## Specificity : 0.5405
## Pos Pred Value : 0.5749
## Neg Pred Value : 0.4987
## Prevalence : 0.5381
## Detection Rate : 0.2870
## Detection Prevalence : 0.4993
## Balanced Accuracy : 0.5370
##
## 'Positive' Class : Yes
##
# model_ctree<-train(target~.,data=trainSet,method='ctree',trControl=fitControl,tuneLength=3)
model_ctree<-train(trainSet[,predictors],trainSet$target,method='ctree',trControl=fitControl,tuneLength=3)
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
#Predicting
testSet$pred_ctree<-predict(object = model_ctree,testSet)
#Checking the accuracy
confusionMatrix(testSet$target,testSet$pred_ctree)
## Confusion Matrix and Statistics
##
## Reference
## Prediction Yes No
## Yes 206 168
## No 169 206
##
## Accuracy : 0.5501
## 95% CI : (0.5136, 0.5861)
## No Information Rate : 0.5007
## P-Value [Acc > NIR] : 0.003801
##
## Kappa : 0.1001
##
## Mcnemar's Test P-Value : 1.000000
##
## Sensitivity : 0.5493
## Specificity : 0.5508
## Pos Pred Value : 0.5508
## Neg Pred Value : 0.5493
## Prevalence : 0.5007
## Detection Rate : 0.2750
## Detection Prevalence : 0.4993
## Balanced Accuracy : 0.5501
##
## 'Positive' Class : Yes
##