Questions tagged [smote]

SMOTE stands for "Synthetic Minority Over-sampling Technique". It is a method to deal with imbalanced data.

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SMOTE parameters optimization problem

I have a date set with 3 imbalanced groups: 10%, 3%, and 88%. I am using the SMOTE algorithm (in the R SMOTE family package) to up-scale the 2 minority groups. I did this twice: dup_size = 3 and 6 ...
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"SMOTE makes the assumption that the instance between a positive class instance and its nearest neighbors is also positive"

I am trying to get my head around this assertion by Liu, Y. et al (2011 pp. 7) about SMOTE oversampling technique that: because SMOTE makes the assumption that the instance between a positive class ...
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SMOTE vs Stratified Sampling in highly imbalanced dataset - classification

I am working on a project with the goal of predicting Cerebral strokes from brain arteries data (speed of blood, resistance etc. of one artery and of the neighboring ones). I have a dataset with ...
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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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Smote algorithm

When our dataset has 5 or more attributes, what will be the method of producing a new sample with Smote algorithm? How will the Euclidean distance with 5 or more attributes be calculated?
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How to use SMOTE on the final model training?

I have three datasets: train, validation, and test (all datasets are labeled). When I have tuned the hyperparameters using random search, I applied SMOTE just on the train data. Now, after I found ...
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Hyperparameter tuning on the training data? Cross validation

I have a set of data (around 1500 data points) with 75 parameters and I am trying to compare the performance of SVM, Decision Tree and a few other supervised techniques. My data set is not perfectly ...
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Does SMOTE overcomplicate cross-validation?

If you create a synthetic dataset based on train, then your independent variable includes the hyperparameters, and the dataset. So you're finding the optimal way of oversampling and the optimal ...
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Applying SMOTE multiple times?

More of a curiosity, but I'm currently learning how to deal with imbalanced datasets and came across the SMOTE method to bias the minority class. The images below show before and after SMOTE was ...
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SMOTE for logistic regression model had a worse result compared to original?

Not sure why using more sample from SMOTE() could lower the overall accuracy: ...
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Adding Noise to continuous and categorical features?

Assume we have a dataset of 10 features, (combination of continuous and categorical features). I wish to add noise to each features separately, can i use the mean and SD of that particular feature to ...
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SMOTEBoost implementation

This question got to do with SMOTEBoost implementation found here but I believe the issue is relayed to imblearn library. I ...
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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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99% data redundancy in binary classification problem

I am working on binary classification problem and there is 99.99% data redundancy. When I looked into the distribution of the classes both seem to be the same. Class imbalance is also part of the ...
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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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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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Designing an experiment to compare how multiple SMOTE variants affect multiple classification models on multiple datasets

For a university paper I want to test a hypothesis that one particular SMOTE variant outperforms two other SMOTE variants. By 'outperforms' I'm looking at using the F1 measure. I want to test this ...
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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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resampling of imbalanced dataset with only binary predictors and target

I am trying to classify an indicator of health as 0 and 1. I have an imbalanced dataset (0 : 5700, 1:1700) where all the values are binary (0 and 1 only for all features and target). I applied many ...
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Performance loss after applying SMOTE

I'm working on a classification problem, and I've an unbalanced dataset, so I applied SMOTE algorithm in order to balance it. While I got an increased performance when working with classification ...
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imbalanced dataset with lots of csv operation (tensorflow,keras)

A project with about 14000 csv files (about 12000 class 0 and 2000 for class 1 for each csv contain 365 columns and 3330 rows (value are either 0 or 1 ) 1.is there any sample code for this kind of ...
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How to generate synthetic data from a balanced dataset?

Let say I have a balanced dataset that has a small training sample size (lack of data). How do I increase the training sample size by generating synthetic data based on the original data? I believe ...
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A question about a logistic regression classifier performance (with and without resampling)

I am working on a dataset with 20 independent variables and 41188 instances. The task is a binary classification where the target variable has 36548 number of no's and 4640 of yes's. I have used ...
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High Validation F1 score but low testing F1 score

I am working on a dataset related to an insurance company and the objective is to predict if the insurance buyer will claim their travel insurance or not. Training data: https://raw.githubusercontent....
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Can oversampling be moved outside stratified k-fold CV?

In a binary classification task, I am using imbalanced-learn's implementation of SMOTENC to oversample the positive class of a very imbalanced dataset. The total number of examples is very high, so ...
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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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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: https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets. ...
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How do I perform a logistic regression w/ SMOTE

I want to understand which variables lead to an infection by parasites in a tree. Hence, I want to use stepwise logistic regression based on AIC. First, I describe what I would do, and then my code ...
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Imbalanced data for multiclass classification with ConvNet

I am trying to apply the SMOTE sampling technique to over-sample the minority class of a multiclass (5-class) problem using the convolutional neural network. As far CNN requirement, the input shape ...
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Improve F1-score for multiclass text classification with highly imbalanced dataset

I am trying to classify clients' complaints with a dataset of 180k complaints. I have 132 classes like this: Counter({'DIAG_000_NODIAG': 66291, 'FORWARD': 29126, 'DIAG_087': 22843, 'DIAG_049': 17668, ...
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SMOTE in decision tree is generating a "Synthetic" rule

I am running a decision tree and to balance the class labels I used SMOTE. The dataset originally consisted of 350k records and after the balancing is 1.400k records, and the resultant decision tree ...
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How to improve Recall and Precision?

I am working on a big data set which has 25 features with 237862 number of rows. I am trying to predict return . 1 is for return and 0 for no return. My data set has 12% of data which returned. So ...
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After applying SMOTE, the class distribution doesn't match the real world. Is this a problem? [duplicate]

I have an extremely unbalanced dataset with two classes: 1: 1,800 # class 1 0: 40,000 # class 0 This is real world customer data of churned/not churned If I ...
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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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2 votes
1 answer
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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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How to implement smogn(smote for regression) in python? [closed]

I have found the paper SMOGN: a Pre-processing Approach for Imbalanced Regression (2017) which gives a github link for code in R. Is there an implementation for smogn in any of the python libraries ...
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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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Problems with SMOTE optimizing function

I am new to machine learning or R and tried to code a function "smotevalue" in R in order to fine-tune the parameters of SMOTE for binary classification/prediction in imbalanced data. The idea is to ...
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Sampling highly imbalance multi-class response variable

I have a dataset (11000 x 117) with response variable having multiple classes. Here is a plot of class distribution: Some of the classes have only 1 sample in the entire dataset and some have 2, 3 ...
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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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Can we apply SMOTE on data with k-fold CV

The SMOTE for the imbalance should be applied for the training data only, right? Can we still do it (perform SMOTE on training data) while we select the k-fold CV and does not go for splitting the ...
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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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4 votes
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PCA, SMOTE and cross validation - how to combine them together?

I was reading a lot recently about PCA and cross validation and it seems that the majority call it malpractice to do PCA before cross validation. I would also like to perform SMOTE, but there is a ...
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5 votes
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Python / Keras: SMOTE and validation_split

I try to train a MLP with an imbalanced dataset. I'd like to use SMOTE to balance my classes; as highlighted here (https://beckernick.github.io/oversampling-modeling/), the class rebalancing should ...
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SMOTE - What is the difference in sampling before or inside train() [closed]

I have an unbalanced dataset and would like to apply SMOTE to the training data. I can either do one of the following: Inside trainControl() add ...
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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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t-test or paired t-test to detect drift for suicide prediction

Context and data I am studying suicides among the military. I created a table that aggregates certain metrics (number of holidays, number of hours worked, etc...) for each officer, for each month ...
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Train on balanced datasets, used for imbalanced datasets?

We usually trained a model using balanced datasets. Even when we do not have a balanced datasets, we will use methods such as SMOTE to create a balanced dataset for training. The question is - how ...
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1 vote
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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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