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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3
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1answer
3k views

SMOTE vs SMOTE-NC for binary classifier with categorical and numeric data

I am using Xgboost for classification. My y is 0 or 1 (true or false). I have categorical and numeric features, so theoretically, I need to use SMOTE-NC instead of ...
0
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1answer
39 views

Model accuracy: how to determine it?

I have some doubts regarding the approach to building a classifier such as Multinomial Naive Bayes or SVM. I will go through the steps to see if the approach is fine. I do have not a lot of experience ...
7
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1answer
883 views

Why you shouldn't upsample before cross validation

I have an imbalanced dataset and I am trying different methods to address the data imbalance. I found this article that explains the correct way to cross-validate when oversampling data using SMOTE ...
1
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3answers
43 views

Test Binary Classifier on a Test-set that includes only one class

I'm working on a disease binary classification problem. 0 = healthy , 1 = not healthy The disease is a movement disorder that appears on the patient while moving a specific movement. I applied leave-...
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0answers
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 ...
2
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0answers
30 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 ...
1
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1answer
2k views

focal loss function help

I am working on a relation extraction and classification problem. The data is in the form of text files. The data is imbalanced. I want to use focal loss function to address class imbalance problem in ...
2
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1answer
265 views

How to weigh imbalanced softlabels?

The target is a probability between N classes, I don't want it to predict the class with the highest probability but the 'actual' probability per class. For example: ...
1
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1answer
41 views

Labels as features in anomaly detection

I have a dataset born to solve a classification problem. Due to the imbalances of the Y, i choose to move to an anomaly detection task. Should I use the Y i have inside the anomaly detection model as ...
1
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0answers
9 views

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 ...
1
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1answer
30 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, ...
0
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1answer
41 views

Which metric should I use for classifying an imbalace data with fewer labels for the negative class?

From reading, I understand that when we have fewer positive class labels, it is better to use precision or recall as the evaluation metric. Which metric should I use when we have fewer negative ...
1
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2answers
306 views

Should augmentation also be performed on the validation set when the dataset is imbalanced?

I am training a CNN on images (2 classes) and I have an imbalanced dataset (1:7 ratio). I am trying to tackle this by performing offline image augmentation. Should I perform augmentation also on the ...
2
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1answer
165 views

Running two stage classification to predict relatively rare event?

I have a very imbalanced sample in which I am trying to predict probability of a rare event (Out of around 25,000 observations, this event is observed around 30 times) and am reluctant to try over/...
3
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3answers
125 views

Overfitted model produces similar AUC on test set, so which model do I go with?

I was trying to compare the effect of running GridSearchCV on a dataset which was oversampled prior and oversampled after the training folds are selected. The oversampling approach I used was random ...
2
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2answers
77 views

Binary classification problem with imbalanced dataset, how to compare to random classifier

We have a very imbalanced dataset (2% of class 1). To the best of our knowledge, there is no baseline in the literature to the problem we want to solve - so we thought of comparing our performance to ...
3
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1answer
60 views

Restrictions on my skewed validation data

I have a severely skewed data sets consisting of 20 something classes where the smallest class contains on the order of 1000 samples and the largest several millions. Regarding the validation data, ...
1
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0answers
412 views

Fraud detection using auto-encoders and Keras

I am following this example to learn a bit about the use of auto-encoders in fraud detection. Now that I reached the end of the article, two questions rose in mind: Can we train the network in an ...
3
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1answer
110 views

Unbalanced data set - how to optimize hyperparams via grid search?

I would like to optimize the hyperparameters C and Gamma of an SVC by using grid search for an unbalanced data set. So far I have used class_weights='balanced' and selected the best hyperparameters ...
0
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1answer
46 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 ...
2
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3answers
3k views

Handling large imbalanced data set

I have an imbalanced data set consisting of some 10's of millions text strings, each with thousands of features created by uni- and bigrams, and additionally I have also the string length and entropy ...
1
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2answers
709 views

Using keras with sklearn: apply class_weight with cross_val_score

I have a highly imbalanced dataset (± 5% positive instances), for which I am training binary classifiers. I am using nested 5-fold cross-validation with grid search for hyperparameter tuning. I want ...
1
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2answers
81 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 ...
1
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0answers
20 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 ...
1
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2answers
49 views

Determining threshold in an area with very few samples of positive label

I have a binary classification task where I want to either keep or discard samples. I have about a million samples, and about 1% should be kept. I want to discard as much as possible, but discarding ...
0
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0answers
14 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 ...
0
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1answer
15 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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0answers
29 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 ...
2
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3answers
1k views

Imbalanced Dataset (Transformers): How to Decide on Class Weights?

I'm using SimpleTranformers to train and evaluate a model. Since the dataset I am using is severely imbalanced, it is recommended that I assign weights to each ...
0
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1answer
46 views

Hypertune xgboost to dealing with imbalanced dataset

My training data has extremely class imbalanced {0:872525,1:3335} with 100 features. I use xgboost to build classification model with bayessian optimisation to hypertune the model in range ...
0
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3answers
125 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 ...
0
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1answer
32 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 ...
0
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1answer
2k views

Class weights for imbalanced data in multilabel problems

I am trying to train a CNN for a multiclass - multilabel classification task (20 classes, each sample can belong to 1+ labels) and the dataset is highly imbalanced. In single-label cases I would use ...
1
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1answer
669 views

bad regression performance on imbalanced dataset

My current dataset has a shape of 5300 rows by 160 columns with a numeric target variable range=[641, 3001]. That’s no big dataset, but should in general be enough for decent regression quality. The ...
1
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1answer
286 views

setting class weights for imbalanced dataset, how using EarlyStopping?

I want to train a CNN with Early Stopping (Keras). The data set is imbalanced, so I have set class_weights to 'balanced' like follows: ...
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0answers
8 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 ...
0
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1answer
54 views

Any pythonic way to auto determine imbalance class problem, specially in multiclass scenario?

A data is imbalanced if a target class proportions are unequal and typically, heavily biased. But, what is the exact measurement of this heavy bias? Before applying imbalance techniques (SMOTE, ADASYN,...
1
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0answers
9 views

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" ...
0
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1answer
48 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 ...
0
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2answers
2k views

What does the classification report interpret? Class 1 indicates abnormal data

How to interpret the report and How is precision, recall values are calculated for individual class labels. What is the significance of macro avg ? Does this report signify a good predictions by the ...
0
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0answers
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 ...
1
vote
1answer
128 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 ...
1
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1answer
325 views

How to weight loss in regression

I've got a regression problem where a model is required to predict a value in the range [0, 1]. I've tried to look at the distribution of the data and and it seems that there are more examples with ...
1
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1answer
197 views

suggestion to implement undersample and oversample

My dataset has the following class distribution ...
0
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0answers
18 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 ...
3
votes
2answers
207 views

High Recall but too low Precision result in imbalanced data

I was training a model using XGBoost Classifier on a heavy imbalanced database with 232:1 of binary class. Because my training data contains 750k rows and 320 features (after doing many feature ...
1
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1answer
39 views

Appropriate Machine Learning algorithm for modeling clustered time-varying binary outcome

I'll just dive right in. I have a decent-size (100K observations) dataset of time-varying continuous and categorical predictors. Categorical predictors, actually, usually do not change, however, ...
1
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0answers
17 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 ...
0
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1answer
98 views

Semi-supervised anomaly detection

I am currently exploring anomaly detection methods for my work and, basically I have gone through Local Oulier Factor and Isolation Forests, both unsupervised methods. Now, the thing is, there might ...
2
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1answer
145 views

How to estimate the accuracy on a large dataset?

Given that I have a deep learning model(handover from former colleague). For some reason, the train/dev set was missing. In my situation, I want to classify my dataset into 100 categories. The ...

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