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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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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97 views

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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9k views

How to print feature names in conjunction with feature Importance using Imbalanced-learn library?

I used BalancedBaggingClassifier from imblearn library to do an unbalanced classification task. How can I get feature improtance of the estimator in conjunction ...
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1answer
412 views

Positively skewed target label in regression

I have a dataset where the target label is positively skewed and produces a long tail, and currently I have a high residual on these values when experimenting with some linear, tree-based and neural-...
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18 views

How to tune the proportion of minority class after oversampling in imbalanced data with cross-validation? [duplicate]

I have an imbalanced data set and I want to balance them with SMOTENC with cross-validation. In order to determine the performance of the classifier on the original data, I will cross-validation as ...
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1answer
43 views

Handling Imbalanced Datasets

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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1answer
107 views

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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4answers
4k views

How to find whether a dataset is blanced or imbalanced?

I have few dataset to experiment classification(Multi-class). These datasets are about 400GB. I wanted to know whether the dataset is balanced or imbalanced. How to know that dataset is balance or ...
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0answers
329 views

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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1answer
107 views

What does IBA mean in imblearn classification report?

imblearn is a python library for handling imbalanced data. A code for generating classification report is given below. ...
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1answer
45 views

The most informative curve for imbalance datasets

For the imbalanced datasets: Can we say the Precision-Recall curve is more informative, thus accurate, than ROC curve? Can we rely on F1-score to evaluate the skillfulness of the resulted model in ...
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1answer
162 views

SMOTE and oversampling with constraints

I'm trying to apply SMOTE to a dataset that has time-constraints. I have information about users visiting a website. For some features, there are time constraints, e.g having the first visit and the ...
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19 views

How many instances should be synthesized for each class when using over-sampling techniques?

As for an imbalanced multi-class dataset, how many instances should be synthesized for each class if we use over-sampling techniques such as SMOTE? For example, there is 4 class including 'A', 'B', 'C'...
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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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50 views

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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1answer
241 views

Combining 'class_weight' with SMOTE

This might sound a weird question, but I could not find enough details in sklearn documentation about 'class_weight'. Can we first oversample the dataset using SMOTE and then call the classifier with ...
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3answers
10k views

imblearn error installing smote

I wanna install smote from imblearn package and I got the Following error: ...
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1answer
499 views

Using SMOTENC in a pipeline

I am trying to figure out the appropriate way to build a pipeline to train a model which includes using the SMOTENC algorithm: Given that the N-Nearest Neighbors algorithm and Euclidian distance are ...
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144 views

Why to adjust class weights instead of simply finding the best threshold?

In a binary supervised classification where classes 1 and 0 have different number of samples in training, it’s very common to find tutorials about adjusting class weights, over and under sampling for ...
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2answers
953 views

For imbalanced classification, should the validation dataset be balanced?

I am building a binary classification model for imbalanced data (e.g., 90% Pos class vs 10% Neg Class). I already balanced my training dataset to reflect a a 50/50 class split, while my holdout (...
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1answer
4k views

SMOTE for regression

I am looking into upsampling an imbalanced dataset for a regression problem (Numerical target variables) in python. I attached paper and R package that implement SMOTE for regression, can anyone ...
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17 views

Low leves of probability observed after modelling.Is it right to scale the probability

I have done modelling on imbalanced class , without any sampling methods. Event rate is around 0.1 ,After modelling I am getting probalities more at the lower side close to zero.I have tried differnt ...
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26 views

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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1answer
354 views

Why is oversampling outperforming class weight?

I have a dataset that is highly imbalanced. One class has 412 (class 0) samples while the other has 67215 (class 1) samples. For its classification, I am using MLP. When I use class weight of 165 for ...
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27 views

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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1answer
45 views

Quantifying the imbalanceness of a dataset

after looking a lot in the literature there is really a lot of how to work with imbalanced dataset but so far I can not find a definition of a imbalanced metric that quantifies how much imbalanced ...
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50 views

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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3answers
4k views

How to Split And Resample Imbalanced Dataset Into Train, Validation and Test

I want to understand how to split the imbalanced data set with a binary target variable where 87% of the samples are negative and 13% of the samples are positive. Now, I know that you should always ...
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1answer
922 views

How to use SMOTENC inside the Pipeline?

I would greatly appreciate if you could let me know how to use SMOTENC. I wrote: ...
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1answer
349 views

Best metric in imbalanced classification for multi-label classification

My test data are imbalanced, i tried to use the precision or the gmean as metrics for a multi-label learning model, but both metrics are not very informative. Is there any way to use for example the ...
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1answer
52 views

Evaluate imbalanced classification model on balanced testing sample

Why it would be too optimistic to compute presicion, recall and f1-score to evaluate a model trained for imbalanced classification on a balanced testing sample ?
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103 views

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 ...
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2answers
144 views

Can we specify the number of data generated(minority class) using SMOTE?

I am trying to improve classification of imbalanced dataset creditcard fraud using SMOTE imbalanced_learn. But, in this it generates the data to 50%, can we give a specific number for the data to be ...
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0answers
43 views

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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1answer
2k views

Difference between sklearn make_pipeline and imblearn make_pipeline

Can anybody please explain the difference between sklearn.pipeline.make_pipline and imblearn.pipeline.make_pipline.
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2answers
355 views

A robust metric in the presence of class imbalance

When evaluating the performance of a multiclass classification problem, on a highly imbalanced dataset, what is the most robust metric for this purpose? I read a paper that states: "Average ...
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2answers
104 views

Dealing with the test set of imbalanced data

I am working on a problem dealing with unbalanced data that has a very specific request. I would like to know the following: When I have an imbalanced dataset and I do train test split, the test ...
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1answer
1k views

Improving accuracy on highly imbalanced dataset

I need some suggestions to improve my model accuracy. The training data shape is : (166573, 14) It has all int and float columns. I have dropped claims_daysaway column as most of values are NaN and ...
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1answer
2k views

Multi class Imbalanced datasets under-sampling imblearn

I have an imbalanced dataset. I am looking to under-sample. Even though, the oversampling process takes less time, the model training takes a lot of time. I have taken a look at imbalanced-learn ...
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3answers
2k views

imbalanced dataset in text classififaction

I have a data set collected from Facebook consists of 10 class, each class have 2500 posts, but when count number of unique words in each class, they has different count as shown in the figure Is ...