Questions tagged [class-imbalance]

Questions referring to classifiers or classifying problems where some of the classes in the data are under-represented.

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11 views

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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19 views

GridSearch CV: Suitable scoring metrics for Imbalanced data sets

I am new to machine learning. This is my $1^{st}$ machine learning project and I am working on classification on an imbalanced dataset. There are also multi-classes in the target variable. I would ...
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1answer
18 views

how to classify highly overlapping data after PCA and t-SNE?

I'm working on a classification (3 classes) of unbalanced weather data having 22 features. Even after applying PCA and t-SNE the data is overlapping. The best classification score achieved so far is ...
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1answer
23 views

Compare model accuracy when training with imbalanced and balanced data

So I was recently doing a data science project which is a multi class classification. The project can be found https://www.kaggle.com/c/otto-group-product-classification-challenge. The dataset is an ...
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9 views

Compare multiple confusion matrix with unbalanced data?

I have a dataset with about 240k points, where the first 30k are "normal" (first class) and all the other 210k are considered anomalies (second class). I have applied on it four different ...
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26 views

Does having two different models improve performance for underrepresented classes?

I am currently working on a dataset that has approximately 7000 annotations, but suffers from severe class imbalance (there are 1331 annotations for the most represented class, and 77 for the least ...
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1answer
42 views

How to improve the result of f1 on imbalanced dataset

I have a dataset in which these are the distribution of the data: Neutral. 15000 Negative 3000 positive 2000 And I am mostly interested to improve the ...
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2answers
125 views

Dealing with unbalanced training set compared with real world data

I am in charge of a fraud detection model that prevents fraudulent users from using our solution. My model is performing great but the issue I have is that the more the model becomes performant the ...
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1answer
30 views

Steps of multiclass classification problem

So this question is more theoretical, than a practical one. I got a dataframe with 4 classes of cars' body types (e.g. sedan, hatchback, etc.) and different characteristics (doors, seats, maximum ...
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17 views

Is it always appropriate to use SMOTE in an imbalanced multiclass dataset?

Is it good practice to always use SMOTE and random undersampling in an imbalanced multiclass dataset or are there exceptions? In context, I am using a traditional machine learning model (SVC) for ...
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For a Multi-class Classification Problem, What are the Pros and Cons of using a Cascade ML model versus Single Multi-class Classification model?

I am developing an ML model for classification using tabular data. It has 5 classes right now and new classes are expected to be continuously added. (Already have a new one leading to an imbalanced ...
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1answer
30 views

Machine learning accuracy for not a class-imbalanced problem

I would like know if the accuracy has an impact on not class-imbalanced dataset ? I know that accuracy is sensitive to class-imbalance and also always good to be able to appreciate precision and ...
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40 views

How do I deal with unbalance classes in a stock market prediction problem?

I am working on a prediction model to predict whether a stock should sell, hold or buy in n days. Each day (or row in the dataset), I classify whether this should ...
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15 views

Precision and Accuracy of a custom Object Detection Models usind networks from TensorFlow Model Zoo

I am trying to develop a model with three classes. To do so, I tried to develop a model with different combinations of the data samples in each class. For example: the $1^{st}$ model has 500 images ...
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87 views

Binary classification with imbalanced dataset, about lightgbm output probability distribution

I trained a binary classifier for an imbalanced dataset. I did two experiments: lightgbm classifier, boosting_type='gbdt', objective='cross_entropy', SMOTE upsample After training the lgbm model, I ...
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Data preparation for password attrition study

Our company sells contracts to businesses to gain access to various reports they might be interested in. A contract lasts one year and consists of a service tier (good, better, best) and a number of ...
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43 views

When should I oversample data?

I am dealing with multi-class classifiers. My data is unbalanced. Hence, I need to apply sampling techniques before training (undersampling or oversampling). When I apply undersampling, ...
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23 views

How does a misrepresented disproportionate data affects modelling?

Let's say I have a dataset of the occurrence of pregnancies each time is tried, the ground truth of success to failure rate is 30:70. But the dataset with me now is a 70:30 dataset. How would that be ...
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17 views

What are the best ways to balance the classes in multilabel classification?

I have around 1000 rows of data with 9 labels. Each label can be either 1 or 0. Out of 9 labels I have 1 label which has 600 1s , 3 labels which have around 300 1s rest are having around 50 1s. I ...
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89 views

Why does the test set class imbalance influences my model's performance?

Considering a balanced training set, I noticed that the results of a classification primarily depend on the class imbalance of the test set. As shown in this article, unless the classes are perfectly ...
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1answer
16 views

Aren't balanced data sets important in regression?

Why is it that the necessity for balanced data sets is (almost) always exclusively mentioned in the context of classification but not of regression?
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30 views

Multiclass classification and imbalanced data

My data has 20k instances of class A and 5 instances of class B. If I am using Cost Sensitive models (Cost-sensitive Logistic Regression, Cost-sensitive Decision Trees etc) for imbalanced datasets, do ...
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15 views

Oversampling on Sequence(Text) data

Has anyone been able to perform synthetic oversampling on Sequential data? From what I've read and understand, the oversampling/undersampling techniques that are currently used are only applicable on ...
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Recall/Precision Metrics on Azure AutoML seem to be oriented to majority class, and I'm trying to focus on minority class

I am running some experiments in Azure using AutoML. My problem is a binary classification one, with highly imbalanced classes (basically trying to predict what factors make a deal "WON" ...
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17 views

Feature Engineering on 3 dimensions data

I'm doing a task where I was given 3 features (a1, a2 and a3) and 3 heavily unbalanced classes. I tried many balancing techniques like SMOTE and undersampling. None of them gives me a reasonable ...
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39 views

Implementing class weighting in Faster RCNN

I have a dataset (around 45,000 screenshots) of UI elements (UI trees containing element types and bounding boxes) and associated screenshots: The dataset is highly imbalanced with the button element ...
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1answer
47 views

Should I resample my dataset?

The dataset that I have is some text data consisting of path names. I am using TF-IDF vectorizer and decision trees. The classes in my dataset are severely imbalanced. There are a few big classes with ...
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18 views

Dependent variable with very few distinct discrete values

I've been reading about different ways to produce models for a dependent variable that has very few distinct discrete values, but haven't found the right fit. I was thinking of using an ordinal logit ...
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15 views

Is there any benchmark dataset for imbalanced text classification?

I would like to work on an unbalanced dataset for text classification (sentiment analysis, intent classifier) and hopefully, come up with an idea to improve the classification on such datasets. Is ...
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14 views

Account for imbalanced data in a Neural Network using prior distribution

I have a dataset with 4 classes, say their distribution in the training-set is $P_{prior}(C1) = 60\% $ $P_{prior}(C2) = 25\% $ $P_{prior}(C3) = 10\% $ $P_{prior}(C4) = 5\% $ After training a Neural ...
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2answers
71 views

Binary Classification with Imbalanced Target [closed]

I have a dataset and my objective is to run a Binary Classification, but my target feature, that is supposed to have "True" and "False", only has "True", as a value. I ...
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1answer
36 views

Evaluation metric for imbalanced data

Hi I'm a CS graduate student I have a question for AI or data experts. I'm writing a paper My dataset is time-series sensor data and anomaly (positive class) ratio is between 5% and 6% you can see the ...
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28 views

What do you suggest to increase the sensitivity rates in conventional ML model?

I have an imbalanced data problem (prop. rate: 0.8571429 0.1428571) and for this reason, our sensitivity and PPV rates are very low. What do you recommend to fix this problem in R or in general? See ...
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1answer
39 views

How to handle imbalanced NLP text data set e.g. some classes only have 2 records

I am working on a dataset with around 2000 records. Around 80% records have their the categorical labels. There are around 200 categories, some categories got more than 20 records; whereas others only ...
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20 views

How to apply class weights for imbalanced data and given misclassification costs?

Say I have a dataset for binary classification with 1000 samples in the minority class and 100000 samples in the majority class and use XGBoost to output probabilities. From an expert I know that ...
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43 views

How to pass manually split data to cross-validation

I have to perform a binary classification. My dataset is quite small 280 samples and quite imbalanced (1:10 ratio). I kept around 100 sample as testing and about 140 for training. My input variables ...
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30 views

Is it right method to remove instances that are hard to predict before train test split?

In a binary classification problem, I have a slightly unbalanced medical dataset with class distribution: 0:5600, 1:1500 0 without a problem and 1 with a problem. I tried many pipelines, automls, and ...
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3answers
64 views

Confusion Matrix before and after SMOTE is same

I am working with a very unbalanced dataset and I used SMOTE (for training data only). However, I did not understand why the results before and after SMOTE are the same. The attached confusion matrix ...
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65 views

How to use SMOTE to rebalance multiclass dataset when the target is one hot encoded with pd.get_dummies?

I'm using a multiclass dataset (cic-ids-2017), which is very imbalanced. I have already encoded the categorical feature (which is the target) using ...
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3answers
126 views

Imbalanced classification with bias

The problem: A business historical heuristic rule for offering a special deal to customers has created a bias in the dataset when trying to use machine learning in order to make a more sophisticated ...
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1answer
30 views

On what threshold we should resample the data?

When working with churn datasets, we usually find imbalanced datasets. My question is how to decide on what basis we should resample the data. For example: while splitting the data before training we ...
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38 views

Is balancing class data for imbalanced problems helpful or just folklore when considering thresholds?

Caveat: I'm aware that imbalanced data questions are a dead horse, but I haven't found an answer to this flavor of it directly. When working with highly imbalanced data (e.g. binary class cases), ...
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24 views

Evaluating a model with different target class distributions between training and testing data

I'm having a bit of an argument about when class imbalances matter when training a classifier, so I was hoping to get some help on understanding a specific concept. Say I have a problem where I want ...
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35 views

XGBoost failing on highly imbalanced data!

I am working on a classification problem, where I am trying to predict a fraud login. The data is highly imbalanced i.e. 0 = non fraud logins , 1 = fraud logins 0 : 4538076 1 : 365 I have been trying ...
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21 views

How to optimize AUPRC for imbalanced data given a precision or recall bias?

My general understanding is that when optimizing a model in an imbalanced class case with a small preferred target class one should optimize first for a model with the best AUPRC (assuming one doesn't ...
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48 views

Train/ Test split on small dataset along with SMOTE

I have a binary classification imbalanced dataset with 1000 samples ( 15% of class 1, 85% of the rest). My main goal is to build a robust classifier using the following approach. Wanted to know if ...
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30 views

Imbalance classes in Named Entity Recognition

I am currently working on a NER problem which attempts to extract 2 entities - place-of-interest(POI) and street from an address string in the Indonesian language. I used IndoBert (available here) and ...
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1answer
44 views

Training is not stable with extreme class imbalance

I'm dealing with a multi-class classification problem with around 30 categories. This problem has a severe class imbalance: Around 300 examples for the least common class. Around 100k examples for ...
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38 views

Using class weight in decision trees with Information Gain

How are weights considered in a decision tree when we want to maximize Information Gain? In other words, what would the entropy calculation become when weights are involved? I can guess either $$ e_1 =...
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1answer
33 views

Data simulation using make_classification in Python

I have a question about data simulation in Python. I deal with the classification of imbalanced data and want to test the effectiveness of different methods on simulated data. I have seen in various ...

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