Should I oversample my validation data to get better F1 score and PRC?
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I am currently working with a dataset that is imbalanced, about 30k rows * 14 features (just for you know), and 99.5% of the data is labeled 0. Since the model is strongly imbalanced I decided to use precision/recall/f1 score to decide the performance of model.
I used SMOTE to oversample my training data (after splitting the validation set out). Now my model is trained with oversampled training data, and I am going to test it with validation set. If I just validate it on original validation data, I get a F1 score around 0.05, and the classification report is followed:
precision recall f1-score support
Class 0 1.00 0.86 0.93 7606
Class 1 0.03 0.75 0.05 36
If I oversample my validation data, I get a F1 score around 0.85:
precision recall f1-score support
Class 0 0.84 0.86 0.85 7606
Class 1 0.86 0.83 0.85 7606
My question is:
Should I use an oversampled validation set? (because the result is much prettier but I think the model is the same anyway)
Why do I have such bad metrics on the original validation data? Is it because the data size is not big enough?
(This is my first time posting so please be gentle if I did or said anything wrong!)
machine-learning classification cross-validation confusion-matrix smote
asked 14 mins ago
Frank XuFrank Xu
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$begingroup$
I am currently working with a dataset that is imbalanced, about 30k rows * 14 features (just for you know), and 99.5% of the data is labeled 0. Since the model is strongly imbalanced I decided to use precision/recall/f1 score to decide the performance of model.
I used SMOTE to oversample my training data (after splitting the validation set out). Now my model is trained with oversampled training data, and I am going to test it with validation set. If I just validate it on original validation data, I get a F1 score around 0.05, and the classification report is followed:
precision recall f1-score support
Class 0 1.00 0.86 0.93 7606
Class 1 0.03 0.75 0.05 36
If I oversample my validation data, I get a F1 score around 0.85:
precision recall f1-score support
Class 0 0.84 0.86 0.85 7606
Class 1 0.86 0.83 0.85 7606
My question is:
Should I use an oversampled validation set? (because the result is much prettier but I think the model is the same anyway)
Why do I have such bad metrics on the original validation data? Is it because the data size is not big enough?
(This is my first time posting so please be gentle if I did or said anything wrong!)
machine-learning classification cross-validation confusion-matrix smote
asked 14 mins ago
Frank XuFrank Xu
11
New contributor
Frank Xu 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 currently working with a dataset that is imbalanced, about 30k rows * 14 features (just for you know), and 99.5% of the data is labeled 0. Since the model is strongly imbalanced I decided to use precision/recall/f1 score to decide the performance of model.
I used SMOTE to oversample my training data (after splitting the validation set out). Now my model is trained with oversampled training data, and I am going to test it with validation set. If I just validate it on original validation data, I get a F1 score around 0.05, and the classification report is followed:
precision recall f1-score support
Class 0 1.00 0.86 0.93 7606
Class 1 0.03 0.75 0.05 36
If I oversample my validation data, I get a F1 score around 0.85:
precision recall f1-score support
Class 0 0.84 0.86 0.85 7606
Class 1 0.86 0.83 0.85 7606
My question is:
Should I use an oversampled validation set? (because the result is much prettier but I think the model is the same anyway)
Why do I have such bad metrics on the original validation data? Is it because the data size is not big enough?
(This is my first time posting so please be gentle if I did or said anything wrong!)
machine-learning classification cross-validation confusion-matrix smote
asked 14 mins ago
Frank XuFrank Xu
11
New contributor
Frank Xu 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 currently working with a dataset that is imbalanced, about 30k rows * 14 features (just for you know), and 99.5% of the data is labeled 0. Since the model is strongly imbalanced I decided to use precision/recall/f1 score to decide the performance of model.
I used SMOTE to oversample my training data (after splitting the validation set out). Now my model is trained with oversampled training data, and I am going to test it with validation set. If I just validate it on original validation data, I get a F1 score around 0.05, and the classification report is followed:
precision recall f1-score support
Class 0 1.00 0.86 0.93 7606
Class 1 0.03 0.75 0.05 36
If I oversample my validation data, I get a F1 score around 0.85:
precision recall f1-score support
Class 0 0.84 0.86 0.85 7606
Class 1 0.86 0.83 0.85 7606
My question is:
Should I use an oversampled validation set? (because the result is much prettier but I think the model is the same anyway)
Why do I have such bad metrics on the original validation data? Is it because the data size is not big enough?
(This is my first time posting so please be gentle if I did or said anything wrong!)
machine-learning classification cross-validation confusion-matrix smote
machine-learning classification cross-validation confusion-matrix smote
asked 14 mins ago
Frank XuFrank Xu
11
New contributor
Frank Xu is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 14 mins ago
Frank XuFrank Xu
11
New contributor
Frank Xu is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 14 mins ago
Frank XuFrank Xu
11
New contributor
Frank Xu is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 14 mins ago
Frank XuFrank Xu
11
asked 14 mins ago
Frank XuFrank Xu
11
11
New contributor
Frank Xu is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Frank Xu is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Frank Xu 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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