Questions tagged [oversampling]

Sampling cases with differential probability, so that classes that occur rarely in the population occur more often in the training data. Does *not* address the problems in unbalanced classes.

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Statistical test when comparing oversampling to no oversampling on ANN

I use 70% of the dataset for training and 30% for testing. I use oversampling on the training dataset with an ANN. I use the test dataset on my ANN and look at the performance of oversampling against ...
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uniformly sample from gaussian distributed data

I have data that is roughly gaussian distributed, bounded on a range of [x0, x1], w/ mean m and stadard deviation s. I want to ...
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Does contamination in the training dataset change the distribution of the predicted probability of the model?

While using a not regularized loss function, oversampled simply binary classification dataset, I observed that whenever I am adding correctly classified examples, there is no change in the ...
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Does threshold on the model probability depend upon the spread in the dataset among positive and negative classes (binary classification)?

I think that the threshold on model probability through which one discern positive (y=0) and negative(y=1) class depends on the spread in the training dataset b/w y=0 and y=1. This question came when ...
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Why is return-weighted (over)sampling making ML result worse?

This is a quantitative finance problem, and I posted this on Quant.Stackexchange as well. However, given that it's a question about the quantitative methods involved (not necessarily something finance-...
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Oversampling in Longitudinal/Panel Data

Would make sense to apply any oversampling (e.g. SMOTE et similia) techniques in order to balance the outcome classes in the context of longitudinal/panel data? Wouldn't such procedures ignore the ...
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How to use SMOTE effectively in below case?

I have an imbalanced multiclass data set, where I am trying to apply SMOTE to synthesis data for minority class. The problem is, for many classes, I have only 1 sample. Even when I try to tackle it ...
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Improving synthetic oversampling with unlabelled data

I am working on a classification problem with a small amount of labelled data (~200 instances) and a larger sample of unlabelled data (~500 instances). To increase the size of the training data I am ...
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ROSE acceptable dispersion/shrinkage

To solve imbalanced data, I used oversampling strategy using ROSE algorithm in Python. As you may know, ROSE is a smoothed bootstrapping method and we can control the dispersion of the augmented data. ...
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How to choose the number of minority class samples to generate in oversampling techniques?

I've been looking into oversampling of the minority class in classification problems. So essentially given a classification problem you generate synthetic samples from the minority class to balance ...
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Threshold / Ratio to consider undersampling / oversampling

I have a classification task (predicting DNA methylation) with a somewhat unbalanced dataset - 38% of values are in the minority class, and the other 62% in the majority class. I have read that one ...
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How to oversample when data needs to be labeled?

I am working on an NLP problem where we obtain labels through a Mechanical Turk–like system. We started with a random sample, experimented with models and determined that rebalancing the minority ...
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How to choose between 2 strategies to train a Deep Learning model on an unbalanced Dataset?

I have a Training Set of respiratory disease sounds, so there are 2 classes: 0 for respiratory sounds of healthy patients. 1 for breathing sounds of patients with a disease. The Training Set is ...
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Getting different results when running SMOTE

I have this code which runs SMOTE and then getting roc_auc_score. The issue is that every I run the code on the same dataset, I get different results. How can I fix this? I need the same sample when ...
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Callibration after oversampling

We have build a credit scoring model with OptBinning library in python. In the process we oversampled the minority class and now we want to callibrate it back to ...
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Random sampling and over sampling

I have binary $\{0,1\}$ classification data that I will use for statistical analysis. The data provider said that the data are randomly drawn from the population and at the same time he said samples ...
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What should be the class imbalance ratio?

I'm working with a really imbalanced binary classification dataset so I decided to use SMOTE for only on the train data. Class rates were 95% -5% before SMOTE and 75-25% after SMOTE. In other words, I ...
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Is balancing class data for imbalanced problems helpful or just folklore when considering thresholds?

(In the context of predictive models) Caveat: I'm aware that imbalanced data questions are a dead horse, but I haven't found an answer to this flavor of it directly. When working with highly ...
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How to determine the correct amount of oversampling (in regression)

For my regression problem I splitted my dataset into a stratified train- and testset (70:30) and I now want to train my models (random forest, gbm, logistic regression) using the trainset. The dataset ...
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Can I inverse the standardscaler after using SMOTE?

As it is written here, you should standardize the data before applying SMOTE. If I inverse the standardscaler action with inverse_transform after using SMOTE, will ...
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Is upsampling a tiny class before cross-validation valid?

I'm working with a dataset containing several classes. The largest class has over 500 samples, and the smallest classes have fewer than 10 samples. I know that you should perform upsampling inside the ...
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How to tune an weighted voting ensemble method?

I am working with a data with 5 unbalanced labels. These codes are contained of Normalization, Oversampling on Feature Engineering part. A list of 9 ordinary Machine Learning methods is provided ...
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Training with oversampling

I'm building a Random Forest model over an unbalaced 4 class dataset. So far I understood how to use oversampling and train my model. My doubt was about when to perform Oversampling. I've already seen ...
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How H2O perform class balancing?

I wanto to perform class balancing using h2o autoML. I know there is a parameter class_sampling_factors that allow to specify the under/over sampling factor for ...
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Limits of oversampling

I have a dataset with an event rate of less than 0.3 percent. To improve the modeling results, I did some oversampling using SMOTE. I initially oversampled so that the event rate increases 10 times to ...
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Will oversampling help with generalization (small imbalanced dataset)?

I have an imbalanced dataset (2:1 ratio) with about 60 patients and 80 features. I performed RFE + stratified cross validation to reduce the features to 15 and I get an AUC of 0.9 with Logistic ...
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Classification on rare events (~%3) and only categorical variables

I need to build a model based on about 10 independent variables, all categorical (only two of which are potentially ordinal), to predict a dichotomous output ('1': 3%; '0': 97%). To overcome the ...
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When is oversampling preferable to undersampling and vice versa?

When data is unbalanced, that is, when the distribution of classes being predicted is very uneven (e.g. 90%/10% for two classes or 10%/15%/75% for three classes), many machine learning models have ...
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SMOTE in unbalanced dataset with binary features

after reading different posts about unbalanced datasets I didn't make my mind clear about my specific problem so that's why I'm posting a new question. In my case, I have a dataset with around 20K ...
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Oversampling methods for numerical data (regression)

There are many oversampling methods for categorical labels (for example SMOTE and Rose, etc.). But, are there oversampling method for numerical labels (the thing that I want to predict with my ...
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Is doing oversampling on train set and undersampling on test set correct?

I have an imbalanced dataset (95% in class 0 and 5% in class 1) and I am using machine learning for classification. The AUC(Area under ROC curve) was high (about 0.86) but AUPRC(Area under precision-...
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3 answers
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Does oversampling/undersampling change the distribution of the data?

I have an imbalanced dataset (10000 positives and 300 negatives) and have divided this into train and test sets. I perform oversampling/undersampling only on the train set since doing this on the test ...
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How do i re-train a final model after using oversampling?

I am a bit puzzled about the process of experimenting with a model and oversampling and then translating it to the final version of the model that will be used: I oversample the data (only the ...
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SMOTE and Lagged Observations

I'm doing a project about the effect of synthetic oversampling in a machine learning context (more precise SMOTE for the oversampling of the minority class of a highly imbalanced target variable). The ...
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2 answers
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Oversampling a multi-labeled data set

Given a data set where each individual data point can be assigned to more than 1 class (a multi-class, multi-label data set), are there any guidelines for calculating oversampling weights, i.e., the ...
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Binary Classification in Imbalanced Data; Oversampling and Imputation

Together with two friends I participate in a university course about data mining in R and we chose the topic of bankruptcy prediction. We started with some "clean" data found on an "In class" kaggle ...
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Run time of SMOTE function in package DMwR

I have a dataframe with 930 000 rows and 220 variables. The objective is a binary classification but my response classes are imbalanced. (88% - 12%) I want to use SMOTE to artificially create ...
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Running SMOTE on unbalanced data

I've read a few answers to similar questions that advise the completion of SMOTE after splitting the data set into Train and Test sets however, the documentation and other examples I've seen run SMOTE ...
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Effects of Oversampling on Linear Regression

We often see that oversampling is a useful technique to combat skewed classes. But there is not much discussion about linear regression. If we just duplicate the original data, and train the linear ...
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influence of oversampling on Semi-supervised multi-label learning

I have suggested a semi-supervised approach for the hierarchical multi-label classification task. I have included the MLSMOTE oversampling technique as a pre-processing step, and then evaluate the ...
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Logistic Regression Class Imbalance and the use of weighting and undersampling

I have been working on a machine learning model using Spark (binomial) LogisticRegression. The dataset has what I think is a high degree of imbalance - roughly 1% of rows are labelled as events. The ...
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1 answer
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Adjusting precision recall curve for oversampling

I built a model for a binary target using oversampled data. The population target prevalence is 0.25. I oversampled to 0.5 by keeping the entirety of the minority class and sampling a portion of the ...
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Running XGBoost with *highly* imbalanced data returns near 0% true positive rate. Tried SMOTE and it did not improve much. What else can I do?

I'm using XGBoost on a dataset of ~2.8M records of hard drive failures, where less than 200 are tagged as failures. After cleaning, there are 11 features in this dataset. Below is my ...
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Oversampling to Create Correlated Feature from Existing Feature

For demo purposes in Python, I would like to create an array or pandas df column that shares a high degree of correlation with an existing array. This is not as easy as it sounds and I don't think it ...
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Imbalanced class SVM prediction results using different validation data

I am trying to fit my data to a classifier using SVM. My data has 2 classes, the positive class which occurs with a probability of 0.002 and the negative class which is the dominant one. Suppose that ...
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While dealing with imbalanced classes, to what extent can we upsample a minority class? [duplicate]

I have my training data with the following approximate distribution: Negative events : 90,000 positive events : 5,000 Training a model would require to oversample the minority class (and might also ...
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Oversampling using SMOTE leading to bad predictions on test set

I have a dataset with an imbalanced binary target. One class accounts for about 94 % of the target variable. I used SMOTE to oversample the minority class but after the oversampling step when I train ...
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Normalization/standardization: Should one do this before oversampling/undersampling the data or after?

When working with imbalanced datasets, should one do one-hot encoding and data standardization before or after sampling techniques (such as oversampling or undersampling)?
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3 votes
1 answer
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How to report the results of a cross-validation to a paper: Can I manually select the best results?

Good morning. With regard to the interpretation of the results of the N-fold cross-validation with over-sampling (SMOTE), I raise the question to write up the paper. Currently, I have performed 5-...
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Oversampling problems in prediction

I have a dataset that contains 284315 samples of class 0 and 492 of class 1. I know, that's huge. I heard about oversampling methods, so I did the following using the RandomOverSampler library: ...
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