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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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, ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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Oversampling SMOTE sampling strategy ratio
I have 36168 data with imbalanced target. 88,3% is 0 (31970 data) and 11,7% is 1 (4198 data).
I want to apply oversampling using SMOTE. Is it ideal to make it the same amount of data so the 0 & 1 ...
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How to choose a machine learning sampling method?
I have a multiclassification problem with a training dataset of 3 groups (50 samples:150 samples: 100 samples).
I have tried comparing models running SMOTE oversampling and class weighting (using ...
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When I use imblearn pipeline instead of sklearn pipeline all textual features disappear. Any solution?
This is my code below, I need to use SMOTENC to balance the dataset, which means I have to use the pipeline from the imblearn library. However, it does not recognize the CountVectorizer features
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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 ...
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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 ...
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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'...
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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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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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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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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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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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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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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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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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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 ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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