Questions tagged [smote]

Synthetic Minority Oversampling Technique (SMOTE) is an approach used for dealing with imbalanced datasets before running them through machine learning models.

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151 views

Why SMOTE is not used in prize-winning Kaggle solutions?

Synthetic Minority Over-sampling Technique SMOTE, is a well known method to tackle imbalanced datasets. There are many papers with a lot of citations out-there claiming that it is used to boost ...
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42 views

Applying SMOTE on time series data

I have a dataset that consist of student grades and it's based on a time series. I used LSTM to predict the student future grade. Can I apply SMOTE on this dataset to ensure that the model will not be ...
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1answer
44 views

SMOTE for multi-class balance changes the shape of my dataset

So I have a dataset of shape (430,17), that consists of 13 classes (imbalanced) and 17 features. The end goal is to create a NN which btw works when I import the imblanced dataset, however when i try ...
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362 views

Imbalanced Dataset: Train/test split before and after SMOTE

This question is similar but different from my previous one. I have a binary classification task related to customer churn for a bank. The dataset contains 10,000 instances and 11 features. The target ...
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1answer
46 views

Is it necessary to use stratified sampling if I am using SMOTE already?

I have already applied SMOTE to my imbalanced dataset with more than 300K observations. Does it still make sense to use stratified K-fold cross validation rather than simply ordinary K-fold cross ...
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10 views

Oversampling techniques for a class with 1 sample

I have 5 classes, one of them having only one sample. I've been researching techniques to oversample such as SMOTE and Bootstrapping but they do not work for the class with only one sample. I am ...
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48 views

SMOTE for Image regression?

Can you use something like SMOTE for an image regression task, where the target value is very skewed and imbalanced? I already tried using classic augmentation techniques like flipping, cropping, etc. ...
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22 views

Training data with one class

I have a real-time scenario, finding out whether a transaction is fraudulent or not. I have a dataset that contains only fraudulent transactions. For any binary classification algorithm, we may need ...
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1answer
73 views

Train score is very lower than Test score, is that normal?

I am working on very imbalanced dataset, I used SMOTEENN (SMOTE+ENN) to rebalance it, the following test is made using Random Forest Classifier : My train and Test ...
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3answers
83 views

Confusion Matrix before and after SMOTE is same

I am working with a very unbalanced dataset and I used SMOTE (for training data only). However, I did not understand why the results before and after SMOTE are the same. The attached confusion matrix ...
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82 views

How to use SMOTE to rebalance multiclass dataset when the target is one hot encoded with pd.get_dummies?

I'm using a multiclass dataset (cic-ids-2017), which is very imbalanced. I have already encoded the categorical feature (which is the target) using ...
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68 views

Train/ Test split on small dataset along with SMOTE

I have a binary classification imbalanced dataset with 1000 samples ( 15% of class 1, 85% of the rest). My main goal is to build a robust classifier using the following approach. Wanted to know if ...
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2answers
66 views

How to properly use oversampling without inflating results?

I am using with a tiny private dataset (over 192 samples) with 4 classes. A preprocessing step is trivial in order to do any classification. Among feature selection and extraction techniques, i ...
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1answer
200 views

How to use SMOTE in Stacking in SKLearn?

I have a data set X,y and split them to train and test data. ...
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381 views

Is it good practice to use SMOTE when you have a data set that has imbalanced classes when using BERT model for text classification?

I had a question related to SMOTE. If you have a data set that is imbalanced, is it correct to use SMOTE when you are using BERT? I believe I read somewhere that you do not need to do this since BERT ...
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18 views

How to create training dataset based on sampled or original data?

I am trying to used SMOTE and Feature Selection by following this paper http://jad.shahroodut.ac.ir/article_825_679b8f128dec2874a8fbc314fc922127.pdf In this paper, the authors have mentioned about 4 ...
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1answer
25 views

Looking for binary class datasets with high class imbalance, that also have intra-class imbalance in the minority class

Newbie question alert... For a college project I want to compare a few variants of SMOTE in terms of how much they improve classification of the minority class, over using random oversampling. I have ...
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1answer
122 views

Why removing rows with NA values from the majority class improves model performance

I have an imbalanced dataset like so: df['y'].value_counts(normalize=True) * 100 ...
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183 views

How do I handle class imbalance for text data when using pretrained models like BERT?

I have a skewed dataset consisting of samples of the form: Category 1 10000 Category 2 2000 Category 3 400 Category 4 300 Category 5 100 The dataset ...
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44 views

Follow up question regarding Upsampling for Imbalanced Data and the use of ADASYN instead of SMOTE

I have a follow-up question regarding this topic. I have been working on a project predicting success(1) or failure(0) for organizations by using the Decision Tree and Random Forest algorithms. My ...
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1answer
269 views

SMOTE train test split with validation data [duplicate]

Would like to ask, in which way to use SMOTE? My dataset is imbalanced and a multiclass problem. As I read in many posts, use SMOTE method only for the training dataset (X_train and y_train). Not for ...
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1answer
52 views

Model to choose with Cross Validation or not?

I made different tests on an imbalanced dataset and got these results: Model 1 = train test validation split + Cross Validation(cv=10) --> f1'micro' 0,95 Model 2 = train test split + smote method ...
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1answer
1k views

Why you shouldn't upsample before cross validation

I have an imbalanced dataset and I am trying different methods to address the data imbalance. I found this article that explains the correct way to cross-validate when oversampling data using SMOTE ...
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214 views

What's the order in applying SMOTE transformation in a pipeline?

Here's the thing, I have an imbalanced data and I was thinking about using SMOTE transformation. However, when doing that using a sklearn pipeline, I get an error because of missing values. This is my ...
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1answer
328 views

SMOTE oversampling for class imbalanced dataset introduces bias in final distribution

I have a problem statement where percentage of goods (denoted by 0) is 95%, and for bads (denoted by 1) it is 5% only. One way is to do under sampling of goods so that model understands the patterns ...
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1answer
867 views

Using SMOTENC in a pipeline

I am trying to figure out the appropriate way to build a pipeline to train a model which includes using the SMOTENC algorithm: Given that the N-Nearest Neighbors algorithm and Euclidian distance are ...
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2answers
166 views

Main options on how to deal with imbalanced data

As far as I can tell, broadly speaking, there are three ways of dealing with binary imbalanced datasets: Option 1: Create k-fold Cross-Validation samples randomly (or even better create k-fold ...
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2k views

How to increase a low recall value?

I am dealing with a HR Attrition Dataset which is highly unbalanced. I used Balancing technique like SMOTE to generate synthetic data and then used Gaussian Naive Bayes to Classify the Attrition. ...
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1answer
232 views

How to best handle imbalanced text classification with Keras?

I implemented a text classification model using Keras. Most of the datasets that I use are imbalanced. Therefore, I would like to use SMOTE to handle said imbalance. I tried both on plain text, and ...
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1answer
118 views

Preferred approaches for imbalanced data

I am building a binary classification model with imbalanced target variable (13% Class 1 vs 87% class 0). I am considering the following three options to handle the data imbalance Option1: Create a ...
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333 views

Imblanced-data: Need assistance with SMOTE technique for a CNN input

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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1answer
213 views

Using MultiLabelBinarizer for SMOTE

This is my first NLP project. I'm trying to use SMOTE for a classifier with 14 classes. I need to convert the classes into an array before using SMOTE. I tried using MultiLinearBinarizer but it does ...
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67 views

Computing Rare values After SMOGN - Imbalanced Regression

I am dealing with a regression problem where I have the phenomenon "Imbalanced Regression". In my problem, the most relevant events are scarcely represented. In order for me to evaluate my models' ...
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502 views

Balancing the dataset using imblearn undersampling, oversampling and combine?

I have the imbalanced dataset: data['Class'].value_counts() Out[22]: 0 137757 1 4905 Name: Class, dtype: int64 ...
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1answer
7k views

SMOTE for regression

I am looking into upsampling an imbalanced dataset for a regression problem (Numerical target variables) in python. I attached paper and R package that implement SMOTE for regression, can anyone ...
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2answers
3k views

SMOTE for multilabel classification

I have a dataset with 77 different labels. Each sample has one or more of these labels. I did some data analysis and found out that the dataset is highly imbalanced - there are a large number of ...
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1answer
328 views

Getting feature vectors from CatBoost pool

I have a dataset with some numerical and categorical features and I am trying to apply CatBoost for categorical encoding and classification. Since my dataset is highly imbalanced, with a large number ...
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1answer
145 views

Unbalanced data set - how to optimize hyperparams via grid search?

I would like to optimize the hyperparameters C and Gamma of an SVC by using grid search for an unbalanced data set. So far I have used class_weights='balanced' and selected the best hyperparameters ...
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2answers
4k views

Passing data to SMOTE after applying train/test split

I'm trying to resample my dataset after splitting it into train and test partitions using SMOTE. Here's my code: ...
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2answers
2k views

Resampling with Python SMOTE

I am trying to do a simple ML re-sampling approach after the train-test split. However when I do this, it throws the below error. Can you please help me understand what this error is about? ...
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1answer
316 views

What are the standard parameters values for SMOTE technique?

I'm working on an imbalanced class data set (200 samples) with 2 classes, first class has 50 sample and second has 150 sample. My questions: When I use SMOTE technique on my data set my total ...
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1answer
169 views

On which step should use SMOTE technique for over sampling?

I want to use SMOTE technique for over sampling but I don't know on which step on pre-processing I should use it. My preprocessing steps are: Missing values Removing Outliers Smoothing Data Should ...
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1answer
179 views

What are advantages of oversampling over changing threshold for unbalanced classes?

Let's say that I have unbalanced data set that has two classes, and I am using Random Forest to make my predictions. Random forest will be biased towards the majority class, which will cause low ...
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1answer
385 views

Adjust predicted probability after smote

i have an imbalance data set and I used smote to oversample the minority class and undersample the majority class. now, I want to check the test AUC using predict_proba of the model. I have two ...
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1answer
206 views

Variation in output of Logistic Regression when using SMOTE

I am working on a logistic regression case with an imbalance in the target variable. To fix this I am using SMOTE (Synthetic Minority Oversampling Technique), but each time I run my regression model, ...
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2answers
3k views

How do I run SMOTE on image data using the packages available?

I need to balance some image datasets, how do I use SMOTE variants or the imblearn SMOTE method with images? I can't figure out how to, since they seem to be working only with numerical datasets.
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2answers
6k views

Oversampling/Undersampling only train set only or both train and validation set

I am working on a dataset with class imbalance problem. Now, I know one needs to oversample or undersample only the train set and not the test set. But my issue is: whether to oversample the train set ...
3
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1answer
4k views

SMOTE vs SMOTE-NC for binary classifier with categorical and numeric data

I am using Xgboost for classification. My y is 0 or 1 (true or false). I have categorical and numeric features, so theoretically, I need to use SMOTE-NC instead of ...
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1answer
347 views

Combining 'class_weight' with SMOTE

This might sound a weird question, but I could not find enough details in sklearn documentation about 'class_weight'. Can we first oversample the dataset using SMOTE and then call the classifier with ...
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45 views

Binary classifier on imbalanced dataset yields weird PR curve

I have a dataset with ~6M points, 9 features and two classes. The minority class represents just under 2% of the data. The data is first divided into 100 batches and a different classifier is trained ...