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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Should I apply the same data transformations in production for my classification model's inference steps

I am now moving my best classification model to production and doing tests currently. Should I use the same scaler() I used in training during my inference in ...
Marc Atanante's user avatar
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Why is my Histogram Gradient Boosting Classifier model still producing type II error? How can I reduce the type II error?

Type 2 error and how to hypertune or feature engineer a solution for it I trial and tested different techniques and kept the structure which made the most sense to me. But still my model confusion ...
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Reduce false positives having imbalanced data

I'm using a DNN-48 having the following scenario: Features: 8 (48 at the end because I generate conditional sequences of 6 elements each) Classes: Y=0 (90%), Y=1 (10%) Precision and recall are good ...
Gabriel's user avatar
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Is there a way to artificially manipulate a dataset in order to replace it for one that gives good predictions?

I'm trying to artificially create a dataset for pure educative reasons but I want it to be based in one particular dataset, the problem is that this original dataset don't make good predictions even ...
CinfaCinfa's user avatar
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2 answers
150 views

Is there any rationale for performing SMOTE-ENN before train-test-split?

I have created a classification model for predicting data, and the problem is that the two classes are highly imbalanced I have a problem. I have created a classification model for predicting data, ...
Hello is me's user avatar
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I have created a classification model for predicting data, and the problem is that the two classes are highly imbalanced [duplicate]

I have a problem. I have created a classification model for predicting data, and the problem is that the two classes are highly imbalanced. So, I dealt with it using the SMOTE+ENN technique. I applied ...
Hello is me's user avatar
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Struggling with understanding RandomForest model with SMOTE

From what I understand my code is telling me that my base model is performing at 96% on it's training data, 55% on it's test data. And my SMOTE model is performing at ~96% on both. From my ...
GroupTheory14's user avatar
5 votes
3 answers
208 views

Reproducible examples where balancing the training data demonstrably improves accuracy

I asked this question on the Statistics SE, but there were no answers, even when a modest bonus was available, so I am asking here to see if any examples can be given. I have been looking into the ...
Dikran Marsupial's user avatar
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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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What is the difference between SMOTE before PCA and after PCA

We all know that PCA (Principal Component Analysis) is a popular statistical tool to reduce the dimensionality in a dataset. SMOTE (Synthetic Minority Over-sampling Technique) allows you to generate ...
Sivadithiyan official's user avatar
1 vote
2 answers
103 views

How to decide the most suitable technique to handle Class Imbalance

For handling an imbalanced dataset, we have a variety of techniques like adjusting class weights, oversampling, undersampling, SMOTE and its different variations (RCSMOTE, GSMOTE, DBSMOTE). My ...
tanmay's user avatar
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Which data hyperparameter tuning using for fit the model

...
Jovian Aditya's user avatar
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SMOTE for image dataset

I'm working on Image augmentation with Smote. I'm confused that how can SMOTE be useful for an image dataset with containing 5955 images with four classes(2552,227,621,2555). Could anyone please help ...
emma Joe's user avatar
6 votes
1 answer
10k views

How does SMOTE work for dataset with only categorical variables?

I have a small dataset of 977 rows with a class proportion of 77:23. For the sake of metrics improvement, I have kept my minority class ('default') as class 1 (and 'not default' as class 0). My input ...
The Great's user avatar
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Can SMOTE be used for non-binary classification?

There is a class imbalance present in my dataset and I would like to balance the dataset. The dependent variable's features are (0,1,2,3,4). How do I make use of SMOTE, SMOTE-N, SMOTE-NC when if they'...
nithin krishna's user avatar
2 votes
2 answers
786 views

Why does class_weight usually outperform SMOTE?

I'm trying to figure out what exactly class_weight from sklearn does. When working with imbalanced datasets, I'm always using ...
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4 answers
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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 ...
Carlos Mougan's user avatar
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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 ...
Mack's user avatar
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1 answer
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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 ...
rSar's user avatar
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4 votes
2 answers
5k 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 ...
KK_o7's user avatar
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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 ...
lostwanderer's user avatar
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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 ...
Yasin's user avatar
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1 vote
1 answer
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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 ...
Mimi's user avatar
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3 answers
311 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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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 ...
Mimi's user avatar
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247 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 ...
Vardaan Khanted's user avatar
1 vote
2 answers
334 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 ...
heresthebuzz's user avatar
1 vote
1 answer
720 views

How to use SMOTE in Stacking in SKLearn?

I have a data set X,y and split them to train and test data. ...
Amin's user avatar
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2 answers
2k 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 ...
QMan5's user avatar
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1 answer
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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 ...
Hoof-Hearted's user avatar
4 votes
1 answer
453 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 ...
sums22's user avatar
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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 ...
nikhil6041's user avatar
1 vote
1 answer
168 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 ...
Ammar Kamran's user avatar
0 votes
1 answer
870 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 ...
martin's user avatar
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-1 votes
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65 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 ...
martin's user avatar
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10 votes
1 answer
3k 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 ...
sums22's user avatar
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1 vote
1 answer
788 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 ...
dummyds's user avatar
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2 votes
1 answer
918 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 ...
Deepak's user avatar
  • 217
2 votes
1 answer
2k 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 ...
thereandhere1's user avatar
2 votes
2 answers
761 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 ...
Newbie's user avatar
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1 answer
5k 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. ...
Hardik Bapna's user avatar
0 votes
1 answer
366 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 ...
Nandini  Matam's user avatar
1 vote
2 answers
206 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 ...
thereandhere1's user avatar
2 votes
0 answers
611 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 ...
arilwan's user avatar
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1 vote
1 answer
336 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 ...
Sanglang's user avatar
1 vote
0 answers
139 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' ...
Perl Del Rey's user avatar
3 votes
0 answers
807 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 ...
hanzgs's user avatar
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4 votes
1 answer
17k 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 ...
thereandhere1's user avatar
5 votes
2 answers
7k 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 ...
Aishwarya A R's user avatar
0 votes
1 answer
604 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 ...
Aishwarya A R's user avatar