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Results tagged with smote
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user 64377
Synthetic Minority Oversampling Technique (SMOTE) is an approach used for dealing with imbalanced datasets before running them through machine learning models.
0
votes
Which data hyperparameter tuning using for fit the model
None:
Certainly not the whole dataset (X,y) because this would cause data leakage and invalidate the evaluation.
The training set (X_train, y_train) should be used only for training.
The solution is …
- 25.9k
7
votes
Accepted
Imbalanced Dataset: Train/test split before and after SMOTE
Essentially applying SMOTE makes the job easier for the model: SMOTE generates artificial instances which tend to have the same properties as each other, so it's easier for the model to capture their patterns … Of course if SMOTE is also applied to the test set, the model appears to perform better. …
- 25.9k
4
votes
Accepted
Is it good practice to use SMOTE when you have a data set that has imbalanced classes when u...
I don't know about any specific recommendation related to BERT, but my general advice is this:
Do not to systematically use oversampling when the data is imbalanced, at least not before specifically …
- 25.9k
2
votes
Accepted
Why removing rows with NA values from the majority class improves model performance
You have a combination of two problems in your data:
imbalance
missing values
In your experiments there's a confusion about what the true distribution of the data is (or should be): either the "real …
- 25.9k
0
votes
SMOTE on training data
I haven't used SMOTE in Weka so I don't know about your specific question, but in general Weka allows you to apply some preprocessing and generate an .arff file as output (for example when doing feature …
- 25.9k
0
votes
Noise Elimination with majority vote filtering
I have a few questions on which I can't find the answers elsewhere
It's probably because there is no simple answer to these three questions :)
I doubt there's any state of the art approach, in s …
- 25.9k