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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After oversampling/undersampling is it always appropriate to adjust probabilities using the odds ratio regardless of the sampling method used?

I have an imbalanced dataset where the target class is <1% of sample. I apply oversampling or undersampling e.g. https://github.com/scikit-learn-contrib/imbalanced-learn. I run random forest on ...
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170 views

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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426 views

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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46 views

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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29 views

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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56 views

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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114 views

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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9 views

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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358 views

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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94 views

Bias-Variance Tradeoff when using Oversampling Technique

Oversampling techniques (e.g. SMOTE) are often used when target values are not approximately equally represented. How does this technique affect bias and variance of the predictive model that is ...
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543 views

random forest imbalanced data-over, under, Smote Sampling

I am using random forest model for an imbalanced dataset. The dependent variable is Yes=73, No=7100. I have 65 independent variables both factor and numeric. I have tried to develop models for ...
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55 views

The problem between over-sampled, down-sampled data and original data using SVM with unbalanced data

I am working on a classifier that is supposed to do binary classification on a dataset includes 5996 examples. ~800 of this examples belongs to class 1 and the rest is class 0. Since there is a huge ...
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67 views

Why over/under-sampling could not help my model fitting?

I fit random forest to my imbalanced dataset with minority class 1. I found that the AUC under the imbalanced data was better than that of re-sampled dataset (over/under sampling). Can someone help to ...
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336 views

Correcting Bias

I have a data set that includes locations of where certain rocks were observed on the Earth. Populated areas have a higher number of observations in general. Remote areas have less observations. I'm ...
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272 views

glmnet- introduce cost function

I'm using glmnet to predict a binary class. I have only 16% of positive cases. Also false negative ( I lose 90 USD) are 9 times more expensive than false positives (...
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15 views

Does oversampling or undersampling not impact the coefficients of independent variables?

I have come across a few discussions on this site which state that random oversampling or undersampling doesn't impact the coefficients of the independent variables in logistic regression. Since the ...
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26 views

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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13 views

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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12 views

How to oversample multivariate time series (sensor failure data)?

Let's say that I have a multivariate time series dataframe of sensors. Each sensor has its serial number, group, several statistics and column failure. Data about sensor's statistics are collected ...
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12 views

Denoising autoencoder with oversampling?

Denoising autoencoder is using noised added training samples to predict (original) training samples themselves. The goal is to denoise when being applied to the real sample. Here is an example of ...
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43 views

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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72 views

Oversampling for imbalanced time series classification

I'm doing multivariate time series classification (two classes) with GRU/LSTM models. Each observation is a multivariate time series with one label (0 or 1). But the two classes are highly imbalanced. ...
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31 views

Is it possible to have overfitting within the first epoch of training?

Usually after training a few epochs we have overfitting and stop the training. But, is there any circumstances or is it possible that overfitting happens within the first epoch of training? Maybe ...
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1answer
218 views

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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56 views

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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1answer
502 views

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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1answer
131 views

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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72 views

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 ...