Difference in model performance measures of train and test data sets
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I am using CART classification technique by dividing a dataset into train and test sets. I have been using Mis-classification error, KS by rank ordering, AUC and Gini as MPMs(model performance measures). The problem I am facing is that the MPM values are quite far apart.
Dataset - https://drive.google.com/open?id=1UwqmM_R3SAHGAn7b1sytdT6KlcWylxUW Metadata - https://drive.google.com/open?id=1PkhvSA4fsuFtZsnaxegI4gBKFEc2Q839
I have tried with minsplit equal to anywhere from 20 to 1400 and minbucket from 5 to 100 but couldn't get expected results. I have also tried oversampling/undersampling through ROSE package but without any improvement. Moreover, the mis-classification error increased a lot. Following code is through which I could get the best values, but they were not enough.
#Reading Data
pdata = read.csv("PL_XSELL.csv", header = TRUE)
#Converting ACC_OP_DATE from type factor to date
pdata$ACC_OP_DATE<-as.Date(pdata$ACC_OP_DATE, format = "%d-%m-%Y")
#Paritioning the data into training and test dataset
set.seed(2000)
n=nrow(pdata)
split= sample(c(TRUE, FALSE), n, replace=TRUE, prob=c(0.70, 0.30))
ptrain = pdata[split, ]
ptest = pdata[!split,]
#CART Model
#Taking the minsplit, minbucket values as low as possible, so that pruning
#can be done later. Higher values didn't allow any scope for pruning
r.ctrl = rpart.control(minsplit=20, minbucket = 5, cp = 0, xval = 10)
#Calling the rpart function to build the tree
cartModel <- rpart(formula = TARGET ~ .,
data = ptrain[,-1], method = "class",
control = r.ctrl)
#Pruning Tree Code
cartModel<- prune(cartModel, cp= 0.00225 ,"CP")
#Predicting class and scores
ptrain$predict.class <- predict(cartModel, ptrain, type="class")
ptrain$predict.score <- predict(cartModel, ptrain, type="prob")
Results that I got-: Train data Mis-classification error-.103 AUC - 0.679 KS - 0.259 Gini - 0.313
Test data Mis-classification error-.113 AUC - 0.664 KS - 0.226 Gini - 0.307
Is it due to the dataset or am I doing something wrong. I am new to Data Analytics. It is a part of my academic project, so I need to use CART technique only. I will put separate questions for Random Forest and Neural Networks. Kindly help.
machine-learning classification r classifier
asked 6 mins ago
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I am using CART classification technique by dividing a dataset into train and test sets. I have been using Mis-classification error, KS by rank ordering, AUC and Gini as MPMs(model performance measures). The problem I am facing is that the MPM values are quite far apart.
Dataset - https://drive.google.com/open?id=1UwqmM_R3SAHGAn7b1sytdT6KlcWylxUW Metadata - https://drive.google.com/open?id=1PkhvSA4fsuFtZsnaxegI4gBKFEc2Q839
I have tried with minsplit equal to anywhere from 20 to 1400 and minbucket from 5 to 100 but couldn't get expected results. I have also tried oversampling/undersampling through ROSE package but without any improvement. Moreover, the mis-classification error increased a lot. Following code is through which I could get the best values, but they were not enough.
#Reading Data
pdata = read.csv("PL_XSELL.csv", header = TRUE)
#Converting ACC_OP_DATE from type factor to date
pdata$ACC_OP_DATE<-as.Date(pdata$ACC_OP_DATE, format = "%d-%m-%Y")
#Paritioning the data into training and test dataset
set.seed(2000)
n=nrow(pdata)
split= sample(c(TRUE, FALSE), n, replace=TRUE, prob=c(0.70, 0.30))
ptrain = pdata[split, ]
ptest = pdata[!split,]
#CART Model
#Taking the minsplit, minbucket values as low as possible, so that pruning
#can be done later. Higher values didn't allow any scope for pruning
r.ctrl = rpart.control(minsplit=20, minbucket = 5, cp = 0, xval = 10)
#Calling the rpart function to build the tree
cartModel <- rpart(formula = TARGET ~ .,
data = ptrain[,-1], method = "class",
control = r.ctrl)
#Pruning Tree Code
cartModel<- prune(cartModel, cp= 0.00225 ,"CP")
#Predicting class and scores
ptrain$predict.class <- predict(cartModel, ptrain, type="class")
ptrain$predict.score <- predict(cartModel, ptrain, type="prob")
Results that I got-: Train data Mis-classification error-.103 AUC - 0.679 KS - 0.259 Gini - 0.313
Test data Mis-classification error-.113 AUC - 0.664 KS - 0.226 Gini - 0.307
Is it due to the dataset or am I doing something wrong. I am new to Data Analytics. It is a part of my academic project, so I need to use CART technique only. I will put separate questions for Random Forest and Neural Networks. Kindly help.
machine-learning classification r classifier
asked 6 mins ago
user2268901user2268901
1
New contributor
user2268901 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am using CART classification technique by dividing a dataset into train and test sets. I have been using Mis-classification error, KS by rank ordering, AUC and Gini as MPMs(model performance measures). The problem I am facing is that the MPM values are quite far apart.
Dataset - https://drive.google.com/open?id=1UwqmM_R3SAHGAn7b1sytdT6KlcWylxUW Metadata - https://drive.google.com/open?id=1PkhvSA4fsuFtZsnaxegI4gBKFEc2Q839
I have tried with minsplit equal to anywhere from 20 to 1400 and minbucket from 5 to 100 but couldn't get expected results. I have also tried oversampling/undersampling through ROSE package but without any improvement. Moreover, the mis-classification error increased a lot. Following code is through which I could get the best values, but they were not enough.
#Reading Data
pdata = read.csv("PL_XSELL.csv", header = TRUE)
#Converting ACC_OP_DATE from type factor to date
pdata$ACC_OP_DATE<-as.Date(pdata$ACC_OP_DATE, format = "%d-%m-%Y")
#Paritioning the data into training and test dataset
set.seed(2000)
n=nrow(pdata)
split= sample(c(TRUE, FALSE), n, replace=TRUE, prob=c(0.70, 0.30))
ptrain = pdata[split, ]
ptest = pdata[!split,]
#CART Model
#Taking the minsplit, minbucket values as low as possible, so that pruning
#can be done later. Higher values didn't allow any scope for pruning
r.ctrl = rpart.control(minsplit=20, minbucket = 5, cp = 0, xval = 10)
#Calling the rpart function to build the tree
cartModel <- rpart(formula = TARGET ~ .,
data = ptrain[,-1], method = "class",
control = r.ctrl)
#Pruning Tree Code
cartModel<- prune(cartModel, cp= 0.00225 ,"CP")
#Predicting class and scores
ptrain$predict.class <- predict(cartModel, ptrain, type="class")
ptrain$predict.score <- predict(cartModel, ptrain, type="prob")
Results that I got-: Train data Mis-classification error-.103 AUC - 0.679 KS - 0.259 Gini - 0.313
Test data Mis-classification error-.113 AUC - 0.664 KS - 0.226 Gini - 0.307
Is it due to the dataset or am I doing something wrong. I am new to Data Analytics. It is a part of my academic project, so I need to use CART technique only. I will put separate questions for Random Forest and Neural Networks. Kindly help.
machine-learning classification r classifier
asked 6 mins ago
user2268901user2268901
1
New contributor
user2268901 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am using CART classification technique by dividing a dataset into train and test sets. I have been using Mis-classification error, KS by rank ordering, AUC and Gini as MPMs(model performance measures). The problem I am facing is that the MPM values are quite far apart.
Dataset - https://drive.google.com/open?id=1UwqmM_R3SAHGAn7b1sytdT6KlcWylxUW Metadata - https://drive.google.com/open?id=1PkhvSA4fsuFtZsnaxegI4gBKFEc2Q839
I have tried with minsplit equal to anywhere from 20 to 1400 and minbucket from 5 to 100 but couldn't get expected results. I have also tried oversampling/undersampling through ROSE package but without any improvement. Moreover, the mis-classification error increased a lot. Following code is through which I could get the best values, but they were not enough.
#Reading Data
pdata = read.csv("PL_XSELL.csv", header = TRUE)
#Converting ACC_OP_DATE from type factor to date
pdata$ACC_OP_DATE<-as.Date(pdata$ACC_OP_DATE, format = "%d-%m-%Y")
#Paritioning the data into training and test dataset
set.seed(2000)
n=nrow(pdata)
split= sample(c(TRUE, FALSE), n, replace=TRUE, prob=c(0.70, 0.30))
ptrain = pdata[split, ]
ptest = pdata[!split,]
#CART Model
#Taking the minsplit, minbucket values as low as possible, so that pruning
#can be done later. Higher values didn't allow any scope for pruning
r.ctrl = rpart.control(minsplit=20, minbucket = 5, cp = 0, xval = 10)
#Calling the rpart function to build the tree
cartModel <- rpart(formula = TARGET ~ .,
data = ptrain[,-1], method = "class",
control = r.ctrl)
#Pruning Tree Code
cartModel<- prune(cartModel, cp= 0.00225 ,"CP")
#Predicting class and scores
ptrain$predict.class <- predict(cartModel, ptrain, type="class")
ptrain$predict.score <- predict(cartModel, ptrain, type="prob")
Results that I got-: Train data Mis-classification error-.103 AUC - 0.679 KS - 0.259 Gini - 0.313
Test data Mis-classification error-.113 AUC - 0.664 KS - 0.226 Gini - 0.307
Is it due to the dataset or am I doing something wrong. I am new to Data Analytics. It is a part of my academic project, so I need to use CART technique only. I will put separate questions for Random Forest and Neural Networks. Kindly help.
machine-learning classification r classifier
machine-learning classification r classifier
asked 6 mins ago
user2268901user2268901
1
New contributor
user2268901 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
user2268901user2268901
1
New contributor
user2268901 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
user2268901user2268901
1
New contributor
user2268901 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
user2268901user2268901
1
asked 6 mins ago
user2268901user2268901
1
1
New contributor
user2268901 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user2268901 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Check out our Code of Conduct.
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