Questions tagged [imbalance]

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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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0answers
14 views

Unseen samples(Rare category) during prediction?

Say if I train on these features(combination of categorical and numerical data), we could see that in feature x2, sample 4 has a rare entry 'b'. if during my train test split, I end up not getting ...
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0answers
70 views

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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0answers
17 views

Unbalanced data set for multi-class classification algorithms

how can unbalanced data set for multi-class classification algorithms in python i am data set a contain three class . for example class1 , ...
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1answer
165 views

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 ...
1
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2answers
885 views

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 ...
3
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1answer
63 views

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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1answer
267 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, ...
3
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1answer
2k views

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 ...