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Questions tagged [class-imbalance]

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15 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
17 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.
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1answer
32 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
28 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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1answer
19 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 ...
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1answer
42 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
18 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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0answers
22 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
71 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
31 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
53 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
40 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
12 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
28 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
25 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
40 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 ...
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2answers
40 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
42 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
40 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 ...
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2answers
193 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
32 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
60 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
16 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 ...
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1answer
152 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
30 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
113 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
19 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 ...
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1answer
156 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
215 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
174 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
58 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
38 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
190 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
563 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 ...
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3answers
208 views

Train classifier on balanced dataset and apply on imbalanced dataset?

I have a labelled training dataset DS1 with 1000 entries. The targets (True/False) are nearly balanced. With sklearn, I have tried several algorithms, of which the GradientBoostingClassifier works ...
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1answer
20 views

How do we go about imbalanced data for prediction problem? [duplicate]

As in classification we have imbalanced classes, we use up-sampling or down-sampling and other techniques, what do we do when we have imbalanced data in prediction problems, for example, I have ...
1
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1answer
205 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 ...
2
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3answers
291 views

imbalanced dataset in text classififaction

I have a data set collected from Facebook consists of 10 class, each class have 2500 posts, but when count number of unique words in each class, they has different count as shown in the figure Is ...
6
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1answer
568 views

Cross validation for highly imbalanced data with undersampling

In my problem, I am dealing with a highly imbalanced data set, say for every positive class there are 10000 negative one. A normal starting method to train a model is to undersample the data. In this ...
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4answers
3k views

Deep network not able to learn imbalanced data beyond the dominant class

I have data with 5 output classes. The training data has the following no of samples for these 5 classes: [706326, 32211, 2856, 3050, 901] I am using the following keras (tf.keras) code: <...
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3answers
48 views

Classifying on imbalanced dataset

I have incidents VS normal operation of my working environment. It is a skew dataset. My prediction accuracy is 95%. Question: 1. Is it common practice among data scientist to accept this prediction? ...
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0answers
17 views

SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?

As per the paper on SmoteBoost, SMOTE is ran for each iteration of the boosting, generating N samples, which are further added to the original training data and the weight distribution of the ...
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1answer
57 views

Class imbalance in one hot encoding for CNN

I am building a 2D Convolutional Neural Network for MFCC features for audio classification. The issue I am facing is that there are 2 classes and huge imbalance between them. One class has 17687 ...
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4answers
885 views

In a binary classification, should the test dataset be balanced?

I have a dataset with 4519 samples labeled as "1", and 18921 samples labeled as "0" in a binary classification exercise. I am well aware that during the training phase of a classification algorithm (...
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1answer
82 views

Best way to deal with realistically imbalanced dataset for Regression problem

I have a dataset where each object has a label between 0-1. Objects with label = 1 are very common but those with label = 0 are very rare. I am interested in predicting the label in unseen data. NOTE:...
4
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1answer
218 views

issue with early-stopping on f1 score with imbalanced data

I have a highly imbalanced dataset with less than 0.5% of the minor class. Using Keras, I'm training DNN on the training set and evaluate performance on validation set. Loss function is ...
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0answers
8 views

Word frequencies in unbalanced case-control dataset

I have a case-control cohort for which I'm doing analysis of clinical notes. The ratio of cases to controls is 1:4. What I'm looking at is the relative frequency of certain words (e.g. overdose, ...
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0answers
53 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 ...

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