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Can you use smote on text data?
But using SMOTE for text classification doesn’t usually help, because the numerical vectors that are created from text are very high dimensional, and eventually using SMOTE, results are just same as if you simply replicate the exact samples to over-sample.
How do you deal with imbalanced data?
Dealing with imbalanced datasets entails strategies such as improving classification algorithms or balancing classes in the training data (data preprocessing) before providing the data as input to the machine learning algorithm. The later technique is preferred as it has wider application.
How are SMOTE and SVM used in text classification?
R: Text classification using SMOTE and SVM. SMOTE algorithm is “an over-sampling approach in which the minority class is over-sampled by creating ‘synthetic’ examples rather than by over-sampling with replacement”. It is a technique used to resolve class imbalance in training data.
How can I apply smote to text classification using Python?
You need to fix the seed number so that you can replicate the result each time you perform the task. HTH. Thank you for your advice, I will try to do that and I hope it will work for me. Thank you, It worked for me. The previous answers are great.
How to use smote for oversampling in Python?
SMOTE, Oversampling on text classification in Python. SMOTE will just create new synthetic samples from vectors. And for that, you will first have to convert your text to some numerical vector. And then use those numerical vectors to create new numerical vectors with SMOTE.
Can you use smote in bag of words?
Text data if represented in the bag-of-words spaces, suffers from higher dimensionality. As we know, Euclidean does not work well in high dimensional spaces, SMOTE generated points may not be very useful. Nonetheless, it may become useful, when text is represented as (neural) embeddings.