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Questions tagged [under-sampling]

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Edited nearest neighbor (ENN) under-sampling? When is that useful?

Tackling the problem of unequal group sizes, one would often intuitively think of increasing the number of observations in the minority group. But sometimes the opposite can also be useful, i.e. ...
Marlon Brando's user avatar
1 vote
1 answer
28 views

How can I undersample the number of data points that are uniformly distributed on a sphere by keeping the uniform distribution?

Given uniform distributed random numbers on a sphere, how can I undersample it, so reduce the number of data points and obtain a subset which keeps the uniform distribution ? I tried to search on ...
HelpNeederStudent's user avatar
2 votes
1 answer
258 views

How to address bias in AI Image Recognition Model: Oversampling, Undersampling, and Ensemble Techniques Not Working

I am currently working on an image recognition project using AI, but I am facing challenges with bias in my model's predictions. The model seems to be biased toward the majority classes in my dataset. ...
Amsyar Nifail's user avatar
1 vote
0 answers
27 views

Is there actually a right and wrong way to deal with major imbalance in logistic regression (or other models, really)? [duplicate]

I have seen a lot of different advice on how to deal with imbalance, and I get that it can be case-specific. But I learned in school that SMOTE oversampling or undersampling were basically the ways ...
Siri C's user avatar
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1 vote
0 answers
41 views

Steps for condensed nearest-neighbor algorithm

I'm in the middle of writing my Master's thesis on undersampling techniques in imbalanced datasets, and I wanted to refer on this paper explaining the Condensed Nearest-Neighbor algorithm and what ...
Maks's user avatar
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2 votes
1 answer
178 views

Do we need preprocessing before applying a sophisticated undersampling method?

My question is around applying undersampling methods to an imbalanced, and highly dimensional dataset, with mixed data. Lets say as an example, I have 150 features, also a highly imbalanced binary ...
Ali Kılınç's user avatar
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0 answers
48 views

How to deal with oversampling and undersampling of countries to be able to compare them?

I have a dataset that looks like this: cc samples population % sample US 100 1,000 1% CA 20 100 20% BR 9 10 90% AU 600 300 200% I have samples from a certain population and the percentage of ...
Rebe's user avatar
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1 vote
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46 views

Undersampling and Oversampling

I've an unbalanced dataset. I need do to perform feature selection and then I'm going to fit my model. Is it conceptually wrong doing undersampling and perform feature selection and then once ...
lorenzlorg's user avatar
1 vote
0 answers
126 views

At what point in the ML pipeline should I under/over sample?

I have an imbalanced multi-class dataset, and am under/over sampling to balance it out. My questions is when should I do this resampling? Should it occur before creating the test set, before creating ...
jda5's user avatar
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1 vote
0 answers
99 views

Underfitting and Overfitting at the same time?

I am using a Logistic Regression Classifier on the Airline Cancellation dataset. Please note that the training set was undersampled (in order to balance classes) while the test set was left as it was. ...
vincenzoconv99's user avatar
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1 answer
45 views

Selection of informative examples of majority class for undersample using SVM

I have this idea in mind, but I am not sure how to implement it. Suppose I have an imbalanced data that I want to down-sample instances of majority class, such that it becomes equal in size to the ...
arilwan's user avatar
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91 views

Undersampling of datasets and training the model using early stopping

I need some clarification on the undersampling of datasets. I have 3 datasets. Undersampled train data, undersampled validation data, and test dataset which is not undersampled and is the true ...
RH1's user avatar
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0 answers
512 views

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 ...
charelf's user avatar
  • 243
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0 answers
937 views

under sampling a multi-label dataset

I have a multi-label dataset, whose label distribution looks something like this, with label on x-axis and number of rows it occurs in the dataset in y-axis. ...
Naveen Reddy Marthala's user avatar
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1 answer
160 views

Should models built using under-sampled data be evaluated against the population

I have a dataset of 11 mil. rows with a 1:10 ratio between minority and majority classes. To train a model, I have selected all the minority class members and 1/3 of the majority class. The ratio is ...
onejerlo's user avatar
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1 answer
106 views

Coefficient estimates of logistic regression after downsampling majority class

I used a binary logit model with a lasso regularization term to predict an unbalanced dataset, where I used undersampling on the minority class (2% of observations) to get a 50/50 split of the classes....
John Locke's user avatar
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1 answer
98 views

Unbalanced dataset classification problem

I have a binary classification problem and I'm working with an unbalanced dataset. The count for each class in the training set looks like: ...
notarealgreal's user avatar
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1 answer
52 views

What are some "not so common" methods for dealing with unbalanced data?

When we talk about unbalanced data, we usually think about SMOTE, resampling and so on. Usually the methods mentioned here. What are others methods you've seem that are not so explored in these ...
Dumb ML's user avatar
  • 217
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0 answers
1k views

What's a range of good F1 scores?

I have watched a lot of videos on machine learning and in terms of F1 scores, all are different. One video says that an F1 score of .8 is bad, but another says an F1 score of .4 is excellent. What's ...
Sriswaroop Koundinya's user avatar
0 votes
0 answers
219 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 ...
Clock Slave's user avatar
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91 views

What causes the high OOB-error for randomForest() in R?

I'm trying to perform a random forest in R on a dataset with 16364 observations (after undersampling), using the function randomForest(). But my results look really weird: What could have caused this?...
AnnieFrannie's user avatar
1 vote
1 answer
2k views

How does R’s randomforest sampsize work?

I am working on a predictive model (imbalanced data) and trying to undersample the majority class data. I wanted to get the representative sample of my majority class and somehow came to know about R'...
Amarpreet Singh's user avatar