Kappa Goes up as Accuracy Goes Down2019 Community Moderator ElectionHow to increase accuracy of classifiers?Kappa From Combined Confusion MatricesKappa near to 60% in unbalanced (1:10) data setVoting combined results from different classifiers gave bad accuracyoversampling plus down sampling using smote not working on random forestsComparing SMOTE to down sampling the majority class in imbalanced binary classificationHow to improve accuracy further for forest cover predictionaccuracy prediction out of sample gone bad ! over fitting ?Low Kappa score but high accuracyExceptionally high accuracy with Random Forest, is it possible?
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Kappa Goes up as Accuracy Goes Down
2019 Community Moderator ElectionHow to increase accuracy of classifiers?Kappa From Combined Confusion MatricesKappa near to 60% in unbalanced (1:10) data setVoting combined results from different classifiers gave bad accuracyoversampling plus down sampling using smote not working on random forestsComparing SMOTE to down sampling the majority class in imbalanced binary classificationHow to improve accuracy further for forest cover predictionaccuracy prediction out of sample gone bad ! over fitting ?Low Kappa score but high accuracyExceptionally high accuracy with Random Forest, is it possible?
$begingroup$
I have recently been trying to train a randomForest model on a binary outcome with a very uneven class split.
282 control ~82%
63 case ~18%
There are a total of 147 predictors that I'm testing for this model.
I have been running Monte Carlo cross validations to determine the best mtry parameter however I have ran into an odd issue.
Based on these plots it looks like my classifier is no better than random (when you consider the class imbalance). The part that confuses me is that for some reason Kappa seems to increase as Accuracy decreases.. which doesn't make a ton of sense to me. Furthermore the graphs seem to suggest that the best mtry in terms of the kappa statistic is equal to the number of predictors, which I am having trouble interpreting the significance of. Does this mean my model is not useful at all for feature importance selection?
machine-learning classification r random-forest unbalanced-classes
asked Mar 21 at 21:48
JTrain500JTrain500
61
New contributor
JTrain500 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 have recently been trying to train a randomForest model on a binary outcome with a very uneven class split.
282 control ~82%
63 case ~18%
There are a total of 147 predictors that I'm testing for this model.
I have been running Monte Carlo cross validations to determine the best mtry parameter however I have ran into an odd issue.
Based on these plots it looks like my classifier is no better than random (when you consider the class imbalance). The part that confuses me is that for some reason Kappa seems to increase as Accuracy decreases.. which doesn't make a ton of sense to me. Furthermore the graphs seem to suggest that the best mtry in terms of the kappa statistic is equal to the number of predictors, which I am having trouble interpreting the significance of. Does this mean my model is not useful at all for feature importance selection?
machine-learning classification r random-forest unbalanced-classes
asked Mar 21 at 21:48
JTrain500JTrain500
61
New contributor
JTrain500 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 have recently been trying to train a randomForest model on a binary outcome with a very uneven class split.
282 control ~82%
63 case ~18%
There are a total of 147 predictors that I'm testing for this model.
I have been running Monte Carlo cross validations to determine the best mtry parameter however I have ran into an odd issue.
Based on these plots it looks like my classifier is no better than random (when you consider the class imbalance). The part that confuses me is that for some reason Kappa seems to increase as Accuracy decreases.. which doesn't make a ton of sense to me. Furthermore the graphs seem to suggest that the best mtry in terms of the kappa statistic is equal to the number of predictors, which I am having trouble interpreting the significance of. Does this mean my model is not useful at all for feature importance selection?
machine-learning classification r random-forest unbalanced-classes
asked Mar 21 at 21:48
JTrain500JTrain500
61
New contributor
JTrain500 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have recently been trying to train a randomForest model on a binary outcome with a very uneven class split.
282 control ~82%
63 case ~18%
There are a total of 147 predictors that I'm testing for this model.
I have been running Monte Carlo cross validations to determine the best mtry parameter however I have ran into an odd issue.
Based on these plots it looks like my classifier is no better than random (when you consider the class imbalance). The part that confuses me is that for some reason Kappa seems to increase as Accuracy decreases.. which doesn't make a ton of sense to me. Furthermore the graphs seem to suggest that the best mtry in terms of the kappa statistic is equal to the number of predictors, which I am having trouble interpreting the significance of. Does this mean my model is not useful at all for feature importance selection?
machine-learning classification r random-forest unbalanced-classes
machine-learning classification r random-forest unbalanced-classes
asked Mar 21 at 21:48
JTrain500JTrain500
61
New contributor
JTrain500 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 21 at 21:48
JTrain500JTrain500
61
New contributor
JTrain500 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 21 at 21:48
JTrain500JTrain500
61
New contributor
JTrain500 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 21 at 21:48
JTrain500JTrain500
61
asked Mar 21 at 21:48
JTrain500JTrain500
61
61
New contributor
JTrain500 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
JTrain500 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
JTrain500 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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