Questions tagged [imbalance]

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under sampling the dataset of multi-label classifiction

I have a multi-label dataset, whose label distribution looks something like this, with label on x-axis and number of rows it occurs in the dataset in y-axis. ...
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Clustering method that allows to choose clusters' size

I have a multilabel dataset showing an extreme case of imbalance. I was thinking of clustering the less populated classes into bigger clusters of size at least N. My question: is there a clustering ...
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GridSearch on imbalanced datasets

Im trying to use gridsearch to find the best parameter for my model. Knowing that I have to implement nearmiss undersampling method while doing cross validation, should I fit my gridsearch on my ...
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Stratified K Fold Cross Validation in Orange: python script

I am using Orange to predict customer churn and compare different learners based on accuracy, F1, etc. As my problem is unbalanced (10% churn - 90% not churn), I want to oversample. However, when ...
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How and where to set weights in case of imbalanced cost sensitive learning in machine learning?

I confront with a binary classification machine learning task which is both slightly imbalanced and cost sensitive. I wonder what (and where in the modeling pipeline, say, in sklearn) is the best way ...
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Imbalanced Dataset (Transformers): How to Decide on Class Weights?

I'm using SimpleTranformers to train and evaluate a model. Since the dataset I am using is severely imbalanced, it is recommended that I assign weights to each ...
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Clustering with imbalanced data and groups

I have a problem that is about identifying clusters of highly correlated items. I initially focused on building a model and features that put similar data items close to each other. The main challenge ...
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  • 325
8 votes
1 answer
276 views

Categorization of approaches to deal with imbalanced classes

What is the best way to categorize the approaches which have been developed to deal with imbalance class problem? This article categorizes them into: Preprocessing: includes oversampling, ...
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3 votes
1 answer
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unbalanced data classification

I used XGBoost to predict company's bankruptcy, which is an extremely unbalanced dataset. Although I tried weighting method as well as parameter tuning, the best result which I could obtain is as ...
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