Questions tagged [smote]

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

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Is it really so bad to do SMOTE on the training set before crossvalidation?

I understand that doing this leads to data leakage, but if I get better performance on the test set does it really matter? I tried using caret with ...
maglorismyspiritanimal's user avatar
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SMOTE and Sequential Feature Selection Order

Good morning, I am doing the following procedure: Split a Train a Test Dataset ...
Andres Portocarrero's user avatar
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low accuracy in random forest model

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Can I use SMOTE technique to increase the number of observations in forecasting problem?

I have done my thesis on forecasting problem in advertisment industry. However, it resulted that the prediction accuracy for traditional econometric models (Lasso, Ridge) showed the better results ...
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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 ...
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ANN uses python smote random oversampling

I did ANN classification using SMOTE random sampling in python but I found strange plot loss and accuracy results. This is my code: ...
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1 vote
1 answer
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Sampling strategies in multi-target classification

I am dealing with multi-target binary classifications (I have two targets). I need to use a sampling strategy. I have tried imblearn.pipeline but I'm getting the same error as this time when I'm ...
Hanna's user avatar
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Order of pre-processing the dataset

suppose I have categorical dataset, I'm doing data pre-processing. what is the correct order of applying these 3 techniques Train Test split SMOTEN to over sampler the minority class Categorical ...
Mohamed Ahmed's user avatar
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1 answer
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SMOTE newly generated rows

I have an unbalanced dataset on which I used SMOTE to even the classes. I'm using R and the package::function is smotefamily::SMOTE. Now, this function required numeric variables and some of the ...
IDK's user avatar
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Is SMOTE any good at creating new points?

Cross Validated has a pretty thorough debunking of class imbalance being an inherent problem for SMOTE to solve. However, SMOTE is explicitly a method for synthesizing new points. Is SMOTE any good at ...
Dave's user avatar
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SMOTE isn't helping with logistic regression reults - for imbalanced data [duplicate]

I have a dataset that is highly imbalanced, talking about 230 cases of class 1 in the target feature, and more than 3800 of class ...
Programming Noob's user avatar
1 vote
1 answer
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Poor model training- what next?

I am trying to train models to predict if somebody will get breast cancer- it is a binary classification problem, using limited features that replicate data a patient's primary care physician will ...
helpwithAI's user avatar
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The order of SMOTE, Feature selection, Model selection?

Please teach me if I am wrong. The appropriate order should be: SMOTE Feature selection (e.g., by using a wrapper method) Model selection (e.g., by selecting the model with highest AUC) Then ...
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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 ...
user3315563's user avatar
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1 answer
144 views

"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 ...
Ahasanul Haque's user avatar
1 vote
1 answer
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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 ...
Amit S's user avatar
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1 vote
2 answers
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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 ...
ryan132442's user avatar
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1 answer
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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: ...
Yanling Meng's user avatar
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2 answers
2k views

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 ...
Suriya Kumar J S's user avatar
2 votes
1 answer
299 views

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 ...
Eliza's user avatar
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1 answer
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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 ...
Anonymous's user avatar
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295 views

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 ...
Amit S's user avatar
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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 ...
DOT's user avatar
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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 ...
Chevady Ju's user avatar
1 vote
3 answers
192 views

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 ...
Aqee's user avatar
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1 vote
1 answer
167 views

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 ...
Coder's user avatar
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2 votes
0 answers
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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....
Aastha Jha's user avatar
3 votes
1 answer
924 views

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 ...
Jonas's user avatar
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1 answer
256 views

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 ...
Mattia Surricchio's user avatar
0 votes
1 answer
50 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
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1 answer
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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 ...
Thomas's user avatar
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1 answer
840 views

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 ...
arilwan's user avatar
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1 vote
0 answers
508 views

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, ...
Mariana Oliva's user avatar
1 vote
1 answer
843 views

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 ...
mauron's user avatar
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1 vote
1 answer
1k views

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 ...
foram224's user avatar
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0 answers
44 views

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 ...
SCool's user avatar
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2 answers
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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 ...
Jose LHS's user avatar
3 votes
0 answers
3k views

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 ...
jennifer ruurs's user avatar
1 vote
0 answers
174 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 ...
mabreitling's user avatar
1 vote
1 answer
619 views

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 ...
Internetmann's user avatar
0 votes
2 answers
2k views

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 ...
Obiii's user avatar
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