Using logLoss as metric function for highly unbalanced dataset
$begingroup$
ihave an highly unbalanced dataset and the caret pacjage only allows me to select accuracy or kappa as performance metric.
Is it correct to use a mlogloss function to compute model performance? Do you have suggestion on how to set other metrics such as the F1?
Here is my code
classifier <- caret::train(x = train ,
y = y_train,
method = 'xgbTree',
metric = 'logLoss',
maximize = FALSE,
importance = TRUE,
trControl = trainControl(
method = 'cv',
number = 10,
search = 'grid',
classProbs = TRUE,
verboseIter = TRUE,
allowParallel = TRUE,
summaryFunction = mnLogLoss
#repeats = 10
),
tuneGrid = expand.grid(nrounds = c(500:800),
max_depth = 6,
eta = 0.01,
gamma = 0,
colsample_bytree = 1,
min_child_weight = 0.6,
subsample = 0.7)
)
machine-learning xgboost unbalanced-classes model-selection metric
asked 2 days ago
3nomis3nomis
1929
$endgroup$
add a comment |
$begingroup$
ihave an highly unbalanced dataset and the caret pacjage only allows me to select accuracy or kappa as performance metric.
Is it correct to use a mlogloss function to compute model performance? Do you have suggestion on how to set other metrics such as the F1?
Here is my code
classifier <- caret::train(x = train ,
y = y_train,
method = 'xgbTree',
metric = 'logLoss',
maximize = FALSE,
importance = TRUE,
trControl = trainControl(
method = 'cv',
number = 10,
search = 'grid',
classProbs = TRUE,
verboseIter = TRUE,
allowParallel = TRUE,
summaryFunction = mnLogLoss
#repeats = 10
),
tuneGrid = expand.grid(nrounds = c(500:800),
max_depth = 6,
eta = 0.01,
gamma = 0,
colsample_bytree = 1,
min_child_weight = 0.6,
subsample = 0.7)
)
machine-learning xgboost unbalanced-classes model-selection metric
asked 2 days ago
3nomis3nomis
1929
$endgroup$
add a comment |
$begingroup$
ihave an highly unbalanced dataset and the caret pacjage only allows me to select accuracy or kappa as performance metric.
Is it correct to use a mlogloss function to compute model performance? Do you have suggestion on how to set other metrics such as the F1?
Here is my code
classifier <- caret::train(x = train ,
y = y_train,
method = 'xgbTree',
metric = 'logLoss',
maximize = FALSE,
importance = TRUE,
trControl = trainControl(
method = 'cv',
number = 10,
search = 'grid',
classProbs = TRUE,
verboseIter = TRUE,
allowParallel = TRUE,
summaryFunction = mnLogLoss
#repeats = 10
),
tuneGrid = expand.grid(nrounds = c(500:800),
max_depth = 6,
eta = 0.01,
gamma = 0,
colsample_bytree = 1,
min_child_weight = 0.6,
subsample = 0.7)
)
machine-learning xgboost unbalanced-classes model-selection metric
asked 2 days ago
3nomis3nomis
1929
$endgroup$
ihave an highly unbalanced dataset and the caret pacjage only allows me to select accuracy or kappa as performance metric.
Is it correct to use a mlogloss function to compute model performance? Do you have suggestion on how to set other metrics such as the F1?
Here is my code
classifier <- caret::train(x = train ,
y = y_train,
method = 'xgbTree',
metric = 'logLoss',
maximize = FALSE,
importance = TRUE,
trControl = trainControl(
method = 'cv',
number = 10,
search = 'grid',
classProbs = TRUE,
verboseIter = TRUE,
allowParallel = TRUE,
summaryFunction = mnLogLoss
#repeats = 10
),
tuneGrid = expand.grid(nrounds = c(500:800),
max_depth = 6,
eta = 0.01,
gamma = 0,
colsample_bytree = 1,
min_child_weight = 0.6,
subsample = 0.7)
)
machine-learning xgboost unbalanced-classes model-selection metric
machine-learning xgboost unbalanced-classes model-selection metric
asked 2 days ago
3nomis3nomis
1929
asked 2 days ago
3nomis3nomis
1929
asked 2 days ago
3nomis3nomis
1929
asked 2 days ago
3nomis3nomis
1929
asked 2 days ago
3nomis3nomis
1929
1929
add a comment |
add a comment |
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