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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1answer
427 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
2k 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
141 views

What is the purpose of 'oversampling' when the test set is still unbalanced?

I understand that both training and testing sets should have the same distribution and also understand that we should not touch the test set (in terms of oversampling). But we know that oversampling ...
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1answer
1k 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
2k 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
168 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
22 views

SMOTE for multi-instance learning i.e num_rows(x_train) > num_rows(y_train)

I have an imbalanced dataset and I wish to predict classes(0 or 1). Sample x_train: ...
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1answer
576 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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1answer
1k views

SMOTE-NC does not help to oversample my mixed continuous/categorical dataset

When I use SMOTE-NC to oversample three classes of a 4-class classification problem, the Prec, Recall, and F1 metrics for minority classes are still VERY low (~3%). I have 32 categorical and 30 ...
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1answer
1k 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
2k 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
7k 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
80 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
274 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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2answers
82 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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2answers
94 views

SMOTE on training data

The SMOTE could only be performed on the training data, so how can we do it using Weka? It means we have to put the training and test data in two separate files and run the SMOTE on the training file, ...
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1answer
328 views

Random forest with zero precision for unbalanced test data

Apologies if this is a basic question. I have a very unbalanced dataset in which the records are labelled by one of two classes, class1 (negative class) and class2 (positive class): class 1: 1.5 ...
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0answers
390 views

How to apply oversampling when doing Leave-One-Group-Out cross validation?

I am working on an imbalanced data for classification and I tried to use SMOTE previously to oversampling the training data. However, this time I think I need to use a leave-on group out (LOGO) cross-...
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1answer
108 views

Class Imbalence Problem even after Balancing Data

So I am training a neural network on a binary classification problem and my Case (1) and Controls (0) were imbalanced so I oversampled my cases so that that the training set was 0.5053 made up of ...
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0answers
32 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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2answers
85 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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1answer
53 views

Class balancing of the dataset

While performing the SMOTE for balancing the class data, what should be the proportion of both class? For instance, if we have 100 instances, what (%) should be the Yes class and what should be the No ...
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2answers
3k views

convert predict_proba results using class_weight in training

As my dataset is unbalanced(class 1: 5%, class 0: 95%) I have used class_weight="balanced" parameter to train a random forest classification model. In this way I penalize the misclassification of a ...
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1answer
182 views

SMOTE and oversampling with constraints

I'm trying to apply SMOTE to a dataset that has time-constraints. I have information about users visiting a website. For some features, there are time constraints, e.g having the first visit and the ...
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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 ...
6
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1answer
7k views

Differences between class_weight and scale_pos weight in LightGBM

I have a very imbalanced dataset with the ratio of the positive samples to the negative samples being 1:496. The scoring metric is the f1 score and my desired model is LightGBM. I am using the sklearn ...
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0answers
34 views

Ignoring unlabeled data for a single class

I have a data set of transactions with a binary flag labeling each as fraud or not fraud. However, it can take up to 90 days for a transaction to reveal itself as fraudulent. Sometimes it happens in a ...
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3answers
300 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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5answers
5k views

Large no of categorical variables with large no of categories

I'm working on a binary classification problem where the dataset is slightly imbalanced (30% class 0 | 70% class 1). Most of my features are categorical with large number of categories. For example: ...
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0answers
51 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
413 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
7k 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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2answers
775 views

When to consider a target Variable unbalanced? [duplicate]

i'm performing a binary classification task , and after cheking the target variable , i saw that i had 69% of 0's and 31% of 1's , so , my question is , do i have in this case a unbalanced target ...
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2answers
124 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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2answers
96 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
53 views

Trying to predict extreme values corresponding to rare events

I need some advice on methodology. I need to predict a numeric value (claim amount) being as good as possible on high values corresponding to rare events (corporal damage, technological disaster...). ...
6
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1answer
752 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
50 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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3answers
1k views

Unbalanced target variable in Orange, how do I balance it?

So I want to perform a predictive model to predict churn. I have 2 datasets, one with churn and the other without (so I can later perform predictions). The issue is that I think my Confusion matrix ...
3
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0answers
379 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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1answer
2k 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
355 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
243 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 ...
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2answers
753 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
85 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
34 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
59 views

oversampling data with subclass

Oversampling of under-represented data is a way to combat class imbalance. For example, if we have a training data set with 100 data points of class A and 1000 data points of class B, we can over ...
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0answers
66 views

Kappa Goes up as Accuracy Goes Down

I have recently been trying to train a randomForest model on a binary outcome with a very uneven class split. 282 control ~82% 63 case ~18% There are a total of 147 predictors that I'm testing for ...
6
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1answer
325 views

How to compare two unsupervised anomaly detection algorithms on the same data-set?

I want to solve an anomaly detection problem on an unlabeled data-set. The only information about this problem is that the anomalies population is lower than 0.1%. It should be notice that the size of ...
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2answers
2k 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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