Questions tagged [oversampling]

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LSTM, seq to classification, why training on balanced data set yields such a good result?

I am using LSTM to classify the origin of people's names. The input data is not balanced over target classes, so I used oversampling to balance it. Now, I defined a simple LSTM model as follows: <...
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unbalanced data on train set and test set

I already have 2 datasets. One to use for training and one for testing. Both datasets are unbalanced (with similar percentages), with around 90% of label 1 . Will it be useful to balance the data if ...
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SMOTENC oversampling without one-hot encoding

I'm using SMOTENC to oversample an imbalanced-dataset. I thought the point of SMOTENC was to give the option to oversample categorical features without one-hot encoding them. The reason I don't want ...
Boots's user avatar
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A question about overfitting and SMOTE

So I understand that overfitting is when you have for example a good accuracy for the training dataset and bad one for the testing dataset, but why would I even check the accuracy for the training ...
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Prior probability shift vs oversampling/undersampling imbalanced datasets

I'm trying to understand what prior probability shift (label drift) in data means. If I understand it correctly then it means that distribution of labels in training dataset differs compared to ...
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Oversampling SMOTE sampling strategy ratio

I have 36168 data with imbalanced target. 88,3% is 0 (31970 data) and 11,7% is 1 (4198 data). I want to apply oversampling using SMOTE. Is it ideal to make it the same amount of data so the 0 & 1 ...
Jovian Aditya's user avatar
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Timing of applying random oversampling on the dataset

I tried to learn classification using machine learning algorithms. I went through Breast Cancer - EDA, Balancing and ML the notebook. In this notebook ...
Encipher's user avatar
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Is my model classification overfitting?

Is this possible to be just a bad draw on the 20% or is it overfitting? I'd appreciate some tips on what's going on.
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Does synthetic data be over sampled as well?

I'm building a binary text classifier, the ratio between the positives and negatives is 1:100 (100 / 10000). By using back translation as an augmentation, I was able to get 400 more positives. Then I ...
guestmember123456790's user avatar
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Explaining the logic behind the pipe_line method for cross-validation of imbalance datasets

Reading the following article: https://kiwidamien.github.io/how-to-do-cross-validation-when-upsampling-data.html There is an explanation of how to use ...
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oversampling multivariate time series data

For some classification needs. I have multivariate time series data composed from 4 stelite images in form of (145521 pixels, 4 dates, 2 bands) I made a classification with tempCNN to classify the ...
ala's user avatar
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How to increase the Accuracy after Oversampling?

The Accuracy before ovesampling : On Training : 98,54% On Testing : 98,21% The Accuracy <...
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How to use SMOTE to rebalance multiclass dataset when the target is one hot encoded with pd.get_dummies?

I'm using a multiclass dataset (cic-ids-2017), which is very imbalanced. I have already encoded the categorical feature (which is the target) using ...
Mimi's user avatar
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How to properly use oversampling without inflating results?

I am using with a tiny private dataset (over 192 samples) with 4 classes. A preprocessing step is trivial in order to do any classification. Among feature selection and extraction techniques, i ...
heresthebuzz's user avatar
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Is it good practice to use SMOTE when you have a data set that has imbalanced classes when using BERT model for text classification?

I had a question related to SMOTE. If you have a data set that is imbalanced, is it correct to use SMOTE when you are using BERT? I believe I read somewhere that you do not need to do this since BERT ...
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