Questions tagged [imbalanced-learn]
Imbalanced Learn is a python package used specifically for dealing with imbalanced data in machine learning contexts.
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Balancing the dataset using imblearn undersampling, oversampling and combine?
I have the imbalanced dataset:
data['Class'].value_counts()
Out[22]:
0 137757
1 4905
Name: Class, dtype: int64
...
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Orange data mining: Balancing data set using imblearn code
I am using an unbalanced dataset. I wanted to oversample my dataset using a Python script (Scripting code for class imbalance in Biolabs Orange). However, it still gives me an error
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Why Imblearn pipeline gives very different results when used scaler and under sampling method swapped
I am using the Kaggle's credit card fraud detection dataset (https://www.kaggle.com/mlg-ulb/creditcardfraud)
In order to create a balanced datasets I was testing RandomUnderSampler() and NearMiss(). I ...
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Multi-classification: low precision due to imbalanced classes in test data - what to do?
I built a multi-classification model with 3 result classes (XGBoost using R's caret-package): A, B and C. I undersampled my training data - so every class is equally abundant for training. The ...
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CART classification for imbalanced datasets with R
Hey guys i need your help for a university project. The main Task is to analyze the effects of over/under-smapling on a imbalanced Dataset. But before we can even start with that, our task sheet says, ...
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Balance two crossentropy losses with different number of neurons
I have a model with a few outputs, each output with shape:
Shape: (batch_size, labels_1) -> softmax -> ...
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Binary classifier on imbalanced dataset yields weird PR curve
I have a dataset with ~6M points, 9 features and two classes. The minority class represents just under 2% of the data. The data is first divided into 100 batches and a different classifier is trained ...
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Different result between Rapidminer and Python imblearn
I'm currently working on imbalanced classification problem. However i found different result between SMOTE in rapidminer and SMOTE in imblearn (python). rapidminer SMOTE give 15-20% improvement on ...
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Preferred approaches for imbalanced data
I am building a binary classification model with imbalanced target variable (13% Class 1 vs 87% class 0). I am considering the following three options to handle the data imbalance
Option1: Create a ...
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Cross validation schema for imbalanced dataset
Based on a previous post, I understand the need to ensure that the validation folds during the CV process have the same imbalanced distribution as the original dataset when training a binary ...
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What is the Near-Miss Version 3 undersampling algorithm?
I understand version 1 and version 2 of the Near Miss undersampling algorithm, as described here. However, I don't understand version 3 of this algorithm. I'll appreciate an explanation.
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Do I need to use AUPRC for reporting classification results on an imbalanced dataset when the model was trained using upsampling and CV
I am working on a binary classification problem which dataset has about 5% of positive class samples. I split the dataset, 70% for training and 30% for testing. I used the test data only once for ...
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How to effectively evaluate a model with highly imbalanced and limited dataset
Most data imbalance questions on this stack have been asking How to learn a better model, but I tend to think one other problem is How do we define "better" (i.e. fairly evaluate the learned ...
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Give more weight to features based on distribution plot
I have a task to predict a binary variable purchase, their dataset is strongly imbalanced (10:100) and the models I have tried so far (mostly ensemble) fail. In ...
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Over-sampling when predicting a contionuous variable
Lets say i am predicting house selling prices (continuous) and therefore have multiple independent variables (numerical and categorical). Is it common practice to balance the dataset when the ...
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Handling Imbalanced Datasets in Orange
I work in the medical domain, so class imbalance is the rule and not the exception. While I know Python has packages for class imbalance, I don't see an option in Orange for e.g. a SMOTE widget. I ...
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How does my score ranges change when I use class weight in Keras vs when I don't?
Following are images of Unweighted Score Range and Weighted Score Range
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Deep learning(MLP) on multiclass classification. Model learns only one class
I am new to deep learning. I have imbalanced class data. I used one hot encoding and scaling to preprocess my data. I have used adamoptimizer as optimizer function and sparse categorical crossentropy ...