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Questions tagged [class-imbalance]

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26 views

How to identify Overfitting in RandomForestClassifier?

Im building a sentiment classification model using RandomForestClassifier. I got the training accuracy of 99.65 & cross-validation( RepeatedStratifiedKFold-5 folds) accuracy of 97.29. I used f1 ...
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1answer
14 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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2answers
20 views

Creating dataset - imbalanced or balanced?

I'm trying to make an image classification model and I have 5 classes - A, B, C, D, E. The goal is to get the highest possible classification accuracy. I have a database of images and I'm selecting ...
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0answers
16 views

smote for imbalance data

I am dealing with data which has only categorical features, whose values are just 0 or 1 and it is imbalance data. So, is it good to try smote because it will try to generate new data points. but, how ...
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12 views

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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0answers
19 views

How can I use the Brier Skill Score in cross-validation for imbalanced data?

I wish to use search for model hyper-parameters (by a grid search) using the Brier score as the scoring method (see code below): ...
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1answer
21 views

Preparing Dataset Minority Class vs Majority Class

I'm currently doing a binary classification for sentiment prediction. Currently I have the majority class (~90% of the data) as my positive class (labelled 1) and the minority class (~10% of the data) ...
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0answers
26 views

Imbalanced classification or Regression? What is the best approach to my A/B testing related problem?

The context of the problem is A/B testing of two new versions of a game. I have a structured dataset (50000 rows x 22 columns) from the game designers that represents data with respect to two versions ...
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1answer
23 views

Is providing class weight to neural network enough for imbalanced binary classification?

I have a highly imbalanced binary classification problem, probably 95:5 for two classes. I don't want to perform resampling as the data is already huge and training it would just take more time. (I'm ...
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2answers
59 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 ...
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3answers
67 views

How to do class balancing?

I am working with a really imbalanced dataset ($\approx$ 1% of positive cases) for a classification problem. I know that class balancing is an important step in this scenario. I have two questions: ...
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2answers
121 views

Cost sensitive learning and class balancing

I am facing a classification problem with classes that are really imbalanced (more or less 1% of positive cases). In addition, the "cost" of a False Negative (FN) is much higher than the ...
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1answer
29 views

Selecting threshold for F1 Score

When selecting a probability threshold to maximize the F1 score prior to deploying a model (based on the precision-recall curve), should the threshold be selected based on the training or holdout ...
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1answer
85 views

SMOTE oversampling for class imbalanced dataset introduces bias in final distribution

I have a problem statement where percentage of goods (denoted by 0) is 95%, and for bads (denoted by 1) it is 5% only. One way is to do under sampling of goods so that model understands the patterns ...
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1answer
15 views

Should the weights for CrossEntropyLoss be exactly the inverse of the propotions of training data?

I have a classifier network which chooses one of three classifications, and uses cross entropy loss as the loss function. If the proportions of training data are 100:10:5 for each classification, ...
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3answers
24 views

Problem of having imbalanced classes in the test set while using oversampling

I have an imbalanced dataset. My classes are 0 and 1. The number of 0 class instances is about 20 times more than the number 1 class instances. I know that I should apply oversampling after train test ...
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0answers
77 views

Image Segmentation Class weight using tensorflow keras

I remember definitely being able to pass a list to class_weight with keras (binary image segmentation specifically). For example: ...
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1answer
46 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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2answers
28 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 ...
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1answer
24 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, ...
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1answer
46 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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0answers
17 views

How do I build a model to improve CTR on campaign?

I am trying to build a propensity model for a client to increase the CTR. Client has the list of people who clicked in the previous campaigns but doesn't have the data on the list of people who didn't ...
2
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2answers
84 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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0answers
25 views

Orange ROC analysis widget

ROC analysis widget has an option to change prior target class probability. I'd like to know, when and how it should be used. Playing with it changes the slope of iso-performance line. Each classifier ...
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2answers
51 views

Data Imbalance in Regression Tasks [closed]

Having read a lot about class imbalance in classification tasks, I'd like to know what is the methodology for data imbalance in regression tasks. Particularly, - What is the procedure to check for ...
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0answers
29 views

What's the best way to validate a rare event detection model during training?

When training a deep model for rare event detection (e.g. sound of an alarm in a home device audio stream), is it best to use a balanced validation set (50% alarm, 50% normal) to determine early ...
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0answers
14 views

loss increases but accuracy and macroF1 are still stable and don't change dramatically

I have a classification task with 2 classes. The dataset is imbalanced. When I train the model, at some point, the loss of test dataset starts to increase but the values of accuracy and macro-f1 don't ...
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1answer
75 views

While downsampling training data should we also downsample the validation data or retain validation split as it is?

I am dealing with class imbalance problem. In this case, I am down sampling the majority class lables in the training set. Among training, validation and test splits, the majority class in training ...
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1answer
14 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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1answer
25 views

Model assessment for EXTREMELY imbalanced dataset

I am dealing with an extremely imbalanced dataset, with about 10,000 negative samples for each positive sample. I am now trying to come up with an adequate measurement of model accuracy but none seem ...
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1answer
24 views

How to handle such a large class imbalance in text data?

I am working on a multi class text classifier. The total number of class that are there is 265 and total number of rows is 20,000. The class with largest number of occurrences has 6000 samples and ...
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1answer
37 views

How to best handle imbalanced text classification with Keras?

I implemented a text classification model using Keras. Most of the datasets that I use are imbalanced. Therefore, I would like to use SMOTE to handle said imbalance. I tried both on plain text, and ...
2
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0answers
30 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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4answers
221 views

How to deal with class imbalance in a neural network?

Suppose we have a game and its action space contains two possible actions: A and B. We have a labelled dataset of state-action ...
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1answer
34 views

How to deal with imbalanced text data

I am working on a problem where I have to classify products into multiple classes (more than one) based on product descriptions. For instance: "Tresemme shampoo and conditioner - sulfate-free" = ...
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0answers
16 views

How to fix class imbalance in dialogue (text) time series data?

I have a dataset that looks like this: ...
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0answers
21 views

dealing with imbalanced data for multi-class problem

Based on the experiments I run for a number of times, and the reading I did on imbalanced data for a multiclassification problem such as this paper, resampling techniques like ...
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0answers
31 views

Identifying possible data leakage

I am building a binary classification model for imbalanced dataset using XGBoost. I tuned the hyperparameters for four different models based on 2 training datasets and 2 optimization metrics. Class ...
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0answers
25 views

Imbalanced text classification by oversampling: correction of class predicted probability by prior probability

My dataset has 3 class and 900 examples for training. Class distribution is 255, 185, and 460. I found that if I oversample (random) the training data then I have to correct/calibrate the predicted ...
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2answers
109 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 ...
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1answer
94 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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1answer
62 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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2answers
37 views

Right order for Data preparation in Machine Learning

For the below mentioned steps of data preparation Outlier detection/treatment Data imputation Data scaling/standardisation Class balancing There are two sub questions Should each of these steps ...
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0answers
20 views

g-mean for binary classification doesn't use sensitivity of each class?

scikit-learn's contrib package, imbalanced-learn, has a function, geometric_mean_score(), ...
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0answers
76 views

Imblanced-data: Need assistance with SMOTE technique for a CNN input

I am trying to apply the SMOTE sampling technique to over-sample the minority class of a multiclass (5-class) problem using the convolutional neural network. As far CNN requirement, the input shape ...
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1answer
135 views

How to apply dataset balancing techniques whilst using Pipeline in Sklearn?

I am new to Machine Learning and trying to construct machine learning models that adhere to good practice and not susceptible to biases. I have decided to use Sklearn's ...
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1answer
3k views

'numpy.ndarray' object has no attribute 'plot' [closed]

I am trying to balance my data set and using imblearn library for this but After performing fit operation when i try to see the data count in dependent variable its showing me below error ...
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0answers
26 views

Computing Rare values After SMOGN - Imbalanced Regression

I am dealing with a regression problem where I have the phenomenon "Imbalanced Regression". In my problem, the most relevant events are scarcely represented. In order for me to evaluate my models' ...
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1answer
26 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 ...
2
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1answer
41 views

Kohen Kappa Coefficient of Naive Bayes with 62% overall accuracy is better than Logistic Regression with 98% accuracy?

I have been trying to evaluate my models used on fire systems dataset with a huge imbalance in the dataset. Most models failed to predict any true positives correctly however naive Bayes managed to do ...

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