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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Reproducible examples where balancing the training data demonstrably improves accuracy
I asked this question on the Statistics SE, but there were no answers, even when a modest bonus was available, so I am asking here to see if any examples can be given.
I have been looking into the ...
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I used SMOTE-ENN to balance my dataset and it improved the performance metrics, but how can I be sure it's not overfitting?
The models were evaluated using 10-fold cross validation.
foldCount = StratifiedKFold(10, shuffle=True, random_state=1)
The models in question are XGBoost.
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How to calculate accuracy of an imbalanced dataset
I like to understand what is the accuracy of an imbalanced dataset.
Let's suppose we have a medical dataset and we want to predict the disease among the patients. Say, in an existing dataset 95% of ...
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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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How to evaluate data imputation techniques
I have a data set with 29 features 8 if them have missing values.
I've tried Sklearn simple imputer and all it's strategies
KNN imputer and several Number of K
Iterative imputer and all combinations ...
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Class imbalance: Will transforming multi-label (aka multi-task) to multi-class problem help?
I noticed this and this questions, but my problem is more about class imbalance. So now I have, say, 1000 targets and some input samples (with some feature vectors). Each input sample can have label ...
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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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Explaining the logic behind the pipe_line method for cross-validation of imbalance datasets
Reading the following article: https://kiwidamien.github.io/how-to-do-cross-validation-when-upsampling-data.html
There is an explanation of how to use ...
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ColumnTransformer worse performance than sklearn pipeline
I have an (unbalanced , binary data) pipeline model consisting of two pipelines (preprocessing and the actual model). Now I wanted to include SimpleImputer into my ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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 ...
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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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imblearn error installing smote
I wanna install smote from imblearn package and I got the Following error:
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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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