I have 7 classes and the total number of records are 115 and I wanted to run Random Forest model over this data. But as the data is not enough to get a high accuracy. So i wanted to apply oversampl...

I have already pre-cleaned the data, and below shows the format of the top 4 rows: [IN] df.head() [OUT] Year cleaned 0 1909 acquaint hous receiv follow letter clerk crown... 1 1909 ask secret...

I'm doing a binary classification with CNNs and the data is imbalanced where the positive medical image : negative medical image = 0.4 : 0.6. So I want to use SMOTE to oversample the positive medic...

How to apply SMOTE algorithm before word embedding layer in LSTM. I have a problem of text binary classification (Good(9500) or Bad(500) review with total of 10000 training sample and it's unbalan...

Problem I am training a deep learning model in PyTorch for binary classification, and I have a dataset containing unbalanced class proportions. My minority class makes up about 10% of the given obs...

I have a very imbalanced dataset. I used sklearn.train_test_split function to extract the train dataset. Now I want to oversample the train dataset, so I used to count number of type1(my data set h...

I wanna simultaneously apply cross-validation and over-sampling. I get the following error from this code: from sklearn.pipeline import Pipeline, make_pipeline imba_pipeline = make_pipeline(SMOTE(...

I'm trying to do some text classification using MultinomialNB, but I'm running into problems because my data is unbalanced. (Below is some sample data for simplicity. In actuality, mine is much lar...

I'm dealing with an imbalanced dataset and want to do a grid search to tune my model's parameters using scikit's gridsearchcv. To oversample the data, I want to use SMOTE, and I know I can include ...

I have a DataFrame in pandas that contain training examples, for example: feature1 feature2 class 0 0.548814 0.791725 1 1 0.715189 0.528895 0 2 0.602763 0.568045 0 3 0.544883 0.925597 0 4 0.42365...