Questions tagged [class-imbalance]

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18 views

Which scoring for GridSearchCV is best, when imbalanced multiclass dataset?

I have an unbalanced multiclass dataset (GTSRB) and want to optimize the hyperparameters of an SVM through GridSearchCV. I know that accuracy is not suitable for scoring in this case. Which evaluation ...
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0answers
9 views

Stratified sampling for large imbalanced data set

I have a very large data set (64 Million Rows) and I want to understand the best approach to sample the data for explorative data analysis before proceeding to perform any classification modeling. ...
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2answers
16 views

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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1answer
18 views

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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1answer
23 views

How to control the amount of positives in classification?

I have a basic, yet quite complex problem to solve right now. Let's say we have a training set of 20,000 samples in my training set, out of which 3 to 4% is flagged as "True", the rest is flagged as "...
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1answer
12 views

Highly Imbalanced dataset fro classes more than 200

I have a text dataset where I need to train a classifier to classify the titles into categories. The dataset shape is more than 575000. There are 256 target classes here. The problem is the dataset is ...
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3answers
71 views

Why did sampling boost the performance of my model?

I have an imbalanced dataset with 88 positive samples and 128575 negative samples. I was reluctant to over/undersample the data since it's a biological dataset and I didn't want to introduce synthetic ...
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0answers
13 views

Clustering in python when imbalanced data sets exist

I have a set of measurements with four features. Two features are continuous (time and distance) and two are discrete. We also know that the population consists of two groups. One is the minority ...
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1answer
27 views

SMOTE vs SMOTENC for binary classifier with categorical and numeric data

I have a problem that I am having trouble thoroughly understanding. I am using Xgboost for classification. My y is 0 or 1 (true or false). I have categorical and numeric features, so theoretically, I ...
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1answer
19 views

Why does downsampling leads classification to only predict one class?

I have a multi-class classification problem. It performs quite well but on the least represented classes it doesn't. Indeed, here is the distribution : And here are the classification results of my ...
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2answers
209 views

Weighted Binary Cross Entropy Loss — Keras Implementation

I have a binary segmentation problem with highly imbalanced data such that there are almost 60 class zero samples for every class one sample. To address this issue, I coded a simple weighted binary ...
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1answer
42 views

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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2answers
128 views

Is There a Way to Re-Calibrate Predicted Probabilities After Using Class Weights?

I have classification data with far more negative instances than positive instances. I have used class weights in my models and have achieved the discrimination I want but the predicted probabilities ...
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1answer
21 views

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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1answer
45 views

Choice of f1 score for highly imbalanced dataset?

I am confused whether to use f1 score with 'micro' average or 'macro' average for better evaluation. Given my dataset is highly imbalanced(600:100000)
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1answer
39 views

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.
1
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1answer
47 views

Oversampling only balances the training set, what about the testing set?

In a case of imbalanced data classification, I know that we only oversample the training set (to prevent data leakage from training to testing subsets), but what if there are no positive data points ...
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2answers
40 views

Resampling for imbalaced datasets: should testing set also be resampled?

Apologies for what is probably a basic question but I have not been able to find a definitive answer either in the literature or in the Internet. When dealing with an imbalanced dataset one possible ...
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2answers
31 views

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 ...
1
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1answer
90 views

ROC AUC score is better if test data is imbalanced

I have an imbalanced dataset and I'm using XGBoost to do binary classification. I used down sampling together with target and one hot encoding for train data. For ...
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0answers
21 views

GridSearch on imbalanced multi-class dataset

I have an imbalanced multi-class dataset (GTSRB) and would like to use GridSearch to determine the hyperparameters for an SVM. As metric for the evaluation I chose F1 with average macro. ...
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1answer
60 views

Choosing weights on random forest for imbalanced data with the aim to minimize false positives

I am currently dealing with a binary classification task on imbalanced data with the following distribution: ...
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1answer
104 views

Poor performance of regression model for imbalanced data

I am trying to train a neural network model to solve a regression problem. The specificity of my dataset is that it has something like an exponential distribution of target values (imbalanced). ...
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1answer
118 views

How does class_weight work in Decision Tree

The scikit-learn implementation of DecisionTreeClassifier has a parameter as class_weight. ...
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1answer
57 views

Machine Learning: Balanced training set but highly unbalanced prediction set? How to adjust?

I am trying to train a model to detect gender in a dataset of CEO speeches. Here are the datasets that I have: Final Dataset: 20K CEO voices analyzed (around 95% male) Testing dataset (?): 1K CEO ...
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0answers
51 views

Best strategy to build Classifier for Mixed Data with class imbalance

I have a dataset which contains : 94 numeric features + 56 categorical features I am trying to build a classifier to predict Target (disease/healthy). 2. Rows : 1812 3. Class imbalance ( Majority ...
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0answers
13 views

How to identify whether or not my dataset is sufficient enough to be learnt from any model at all?

I created my own bipartite dataset where 1 group consists of disease and another group consists of genes. The number of diseases is significantly lower that number of genes. Furthermore, one disease ...
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2answers
34 views

Low prediction/classification accuracy due to imbalance in data feeding

I am building the neural network for image analysis to do Chest Xray classification (Abnormal/Pass). The classification accuracy for abnormal Xray is low, I guess it is due to the lack of abnormal ...
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0answers
27 views

Problem On Class Imbalanced Data

I am getting an F-Score of 0.99 on the train_test_split data, but only getting 0.40 for a competition's test data. I am oversampling with random forest (after ...
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1answer
42 views

Which classification algorithms are negatively affected by class imbalances?

I've seen a few posts and papers floating around the web (mostly those related to over/undersampling, SMOTE, and cost-sensitive training) that, when discussing class imbalance, specify that certain ...
1
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2answers
54 views

Clustering on imbalanced data that has high correlation

I am clustering images of two categories, but for the purposes of the experiment, I do not know the labels i.e. this is an unsupervised problem. Via ...
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0answers
51 views

Metrics parameter in model.compile function in Keras for imbalanced data set

Accuracy for an imbalanced data set is not relevant and therefore I use precision and recall to evaluate my model. However, whenever I train a model in Keras a metrics parameter must be specified in ...
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0answers
22 views

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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2answers
46 views

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 ...
2
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2answers
360 views

Why class weight is outperforming oversampling?

I am applying both class_weight and oversampling (SMOTE) techniques on a multiclass classification problem and getting better results when using the class_weight technique. Could someone please ...
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0answers
44 views

Why does Logistic Regression perform better than Autoencoders when classifying imbalanced data?

The 'shuttle' data can be downloaded from the link here. It is imbalanced data and there are two classes in the target variable. The proportion of the two classes are seven percent. I used Logistic ...
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2answers
63 views

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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1answer
19 views

Forcing class imbalance to mirror the target data

I'm trying to do binary classification on some data, my source data has a class split of 40% A / 60% B while my target data has a split of 70% A / 30% B. Is it a worthwhile strategy to use SMOTE to ...
6
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1answer
177 views

Imbalanced classes (balance of train, validation, and test)

1) I am currently trying to set up a feedforward neural network with highly imbalanced classes (binary classification) in which the number of observations of class 1 is very low (and the class of ...
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0answers
33 views

Suggestion for model performance improvement for ML competition

I am working on highly imbalanced dataset and trying to increase accuracy(metric: roc_auc) of my model which is hovering around 82-83%. This is part of an internal ...
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0answers
172 views

How to explain a Calibration Plot for many models?

I have a heavy imbalanced dataset with a classification problem. I try to plot the Calibration Curve from the sklearn.calibration package. In specific, I try the ...
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0answers
26 views

UnderBagging Testing in Matlab

I used UnderBagging for an imbalanced dataset with 45700 observation with 20 fetures. 45000 observations are 1 and 700 are 0. I used UnderBagging for classifier C ( for example for Decision Tree). I ...
1
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1answer
291 views

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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0answers
232 views

Adjust class weights due to class imbalance and class importance Multi class classification XGBoost

With respect to this question and the answer given by @Esmailian, Would anyone be able to let me know if Class B has a higher importance or the positive class ( i.e. it needs to have a higher ...
6
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2answers
178 views

Why real-world output of my classifier has similar label ratio to training data?

I trained a neural network on balanced dataset, and it has good accuracy ~85%. But in real world positives appear in about 10% of the cases or less. When I test network on set with real world ...
2
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1answer
115 views

Dealing with biased binary classifier

My training data is weighed heavier on the '1' class, with about a 4:6 ratio. This outputs a classifier that is of 82% accuracy with an emphasis on the '1' class, which makes sense. ...
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0answers
41 views

intercept correction in deep learning

Say I have an imbalanced data set, and I decided to over/undersample it during model training. I would then like to predict on new records but using the original, true imbalance in the classes as an ...
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0answers
31 views

Best approach for classification problem where examples most belong to one set

I'm working to build an opt-out filter for my company. I have a small amount of machine learning experience (I've done a few projects with tensorflow in the past), but wanted to get other opinions on ...
2
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2answers
255 views

How correctly assign weights to minority class or samples in ANN?

Having an imbalanced dataset. Abnormal class rate is %5. To handle with the problem I have gave extra weight to the abnormal class. However, It did not change anything. Here is my code: ...
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1answer
866 views

CNN - imbalanced classes, class weights vs data augmentation

I have a set of data with a few strongly imbalanced classes, eg the smallest class is about 54 times smaller than the largest. Therefore, data augmentation in order to equalize the size of classes ...