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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21 views

Imbalanced NLP text classification

I'm trying to solve a multi-class text classification task with 3 classes. I have an initial pretty balanced but small dataset. When I start to mine additional data I can't always find a lot of new ...
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11 views

What are some strategies to deal with label sparsity when training a protein function prediction model?

The protein function prediction task requires you to take a sequence of amino acids (think words in a sentence, but if there are only 20 words), and output the functions that protein can take. There ...
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281 views

Imbalanced Dataset: Train/test split before and after SMOTE

This question is similar but different from my previous one. I have a binary classification task related to customer churn for a bank. The dataset contains 10,000 instances and 11 features. The target ...
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1answer
21 views

roc_auc_score from sk-learn gives error when test label vector with classes has only a subset of the whole set

I have an imbalanced dataset. Does it make sense to compute the roc-auc for the classifier I created in a holdout set? Here's very artificial MWE: ...
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12 views

Should I use Pad Sequence when using Word Vectors?

I have an unbalanced text data set. I want to use word vectors to embed words. When I use pad sequence? Before or after the word vector? I tried it, after the word vector I used pad sequence but my ...
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1answer
50 views

Imbalanced classification task – Discrepancy between learning curves and test set evaluation

I have a binary classification task related to customer churn for a bank. The dataset contains 10,000 instances and 11 features. The target variable is imbalanced (80% remained as customers (0), 20% ...
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10 views

Class Weight in sklearn DecisionTreeClassifier impact during prediction

I understand that class weights are used during splitting to weigh whatever metric in the children of the split. However I cannot find anywhere whether class weights also impact prediction or are ...
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13 views

Train Test Split for Imbalance Data set for credit card transaction data set

I am currently working on a credit card transaction datasets for fraud detection, and I am unsure how to go about splitting the data. Transactions are time related data, do I split them like how you ...
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21 views

Does class weighting encourage overfitting when the true class distribution is imbalanced?

I am working on a classification problem in which ~90% of samples come from class 1 while ~10% of samples come from class 2. I have been using various techniques to combat the class imbalance while ...
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Imbalanced Classification: BOW vs doc2Vec in XGBoost with sample weights

I am new to machine learning. I have an imbalanced dataset of pages of reports with class 1: 97%, class 2: 2.2% class 3: 0.25% which are the different type of pages I am mostly concerned with ...
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32 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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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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30 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
30 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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11 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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28 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
47 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
130 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
32 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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25 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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12 views

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
31 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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44 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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179 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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1answer
51 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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19 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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93 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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31 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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35 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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57 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
48 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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17 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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15 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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77 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
38 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
45 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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21 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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56 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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32 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
77 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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72 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
129 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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