Questions tagged [oversampling]
The oversampling tag has no usage guidance.
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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 ...
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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 ...
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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 ...
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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.
user135670
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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 ...
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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 ...
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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 ...
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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 ...
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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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