RFECV converged and stagnate between 30 to 40 features but best model had 140 features
I have 145 features on 7800 samples and I am trying to use RFECV to train Gaussian Naive Bayes, Logistic Regression, and Linear SVM.
Looking at the graph above, both the LR and SVM models starts to stagnate between 30 to 40 features. Is this a sign of overfitting? It still spat out 140 features for both models since RFECV would only tell you the feature set which achieved the highest accuracy. There is only a subtle accuracy differences between 30 to 140 features, so what is the smartest thing to do here? Do I just apply the same number of features which the Naive Bayes had on both LR and SVM, (which is 39 features in this case)?
Thanks
feature-selection rfe
asked 20 hours ago
chmscrbbrfckchmscrbbrfck
177
add a comment |
I have 145 features on 7800 samples and I am trying to use RFECV to train Gaussian Naive Bayes, Logistic Regression, and Linear SVM.
Looking at the graph above, both the LR and SVM models starts to stagnate between 30 to 40 features. Is this a sign of overfitting? It still spat out 140 features for both models since RFECV would only tell you the feature set which achieved the highest accuracy. There is only a subtle accuracy differences between 30 to 140 features, so what is the smartest thing to do here? Do I just apply the same number of features which the Naive Bayes had on both LR and SVM, (which is 39 features in this case)?
Thanks
feature-selection rfe
asked 20 hours ago
chmscrbbrfckchmscrbbrfck
177
add a comment |
I have 145 features on 7800 samples and I am trying to use RFECV to train Gaussian Naive Bayes, Logistic Regression, and Linear SVM.
Looking at the graph above, both the LR and SVM models starts to stagnate between 30 to 40 features. Is this a sign of overfitting? It still spat out 140 features for both models since RFECV would only tell you the feature set which achieved the highest accuracy. There is only a subtle accuracy differences between 30 to 140 features, so what is the smartest thing to do here? Do I just apply the same number of features which the Naive Bayes had on both LR and SVM, (which is 39 features in this case)?
Thanks
feature-selection rfe
asked 20 hours ago
chmscrbbrfckchmscrbbrfck
177
I have 145 features on 7800 samples and I am trying to use RFECV to train Gaussian Naive Bayes, Logistic Regression, and Linear SVM.
Looking at the graph above, both the LR and SVM models starts to stagnate between 30 to 40 features. Is this a sign of overfitting? It still spat out 140 features for both models since RFECV would only tell you the feature set which achieved the highest accuracy. There is only a subtle accuracy differences between 30 to 140 features, so what is the smartest thing to do here? Do I just apply the same number of features which the Naive Bayes had on both LR and SVM, (which is 39 features in this case)?
Thanks
feature-selection rfe
feature-selection rfe
asked 20 hours ago
chmscrbbrfckchmscrbbrfck
177
asked 20 hours ago
chmscrbbrfckchmscrbbrfck
177
asked 20 hours ago
chmscrbbrfckchmscrbbrfck
177
asked 20 hours ago
chmscrbbrfckchmscrbbrfck
177
asked 20 hours ago
chmscrbbrfckchmscrbbrfck
177
177
add a comment |
add a comment |
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