Questions tagged [smote]

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

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Steps for a highly imbalanced classification steps. Should I up-sample & under-sample data or just up-sample the imbalanced class

I have a highly imbalanced binary (yes/no) classification dataset. The dataset currently has appx 0.008% 'yes'. I need to balance the dataset using SMOTE. I came across 2 method to deal with the ...
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34 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 ...
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18 views

CART necessary for Over/Under-Sampling in R?

i need your help. Recently, i talked to a good friend of mine, and somehow we came across the over/under-sampling topic. We both have same experience in those topics, but then he told me he always "...
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71 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, ...
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119 views

SMOTE for multi-label 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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58 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 ...
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36 views

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

I would like to optimize the hyperparameters C and Gamma of an SVC (SVM scikit-learn) by using grid search for an unbalanced data set. So far I have used class_weights='balanced' and selected the best ...
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1answer
39 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 ...
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11 views

Test and Validation datasets for imbalance classification tasks using SMOTE or other over/under methods

I was reading up more on class imbalance and over/under-sampling methods to help reduce the imbalance and improve ROC. I am working on an extremely imbalanced dataset for click-prediction, so like ...
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39 views

Oversampling for imbalanced time series classification

I'm doing multivariate time series classification (two classes) with GRU/LSTM models. Each observation is a multivariate time series with one label (0 or 1). But the two classes are highly imbalanced. ...
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37 views

Imbalance class data resample gets results in overfitting Random Forest

I am working with a very imbalanced dataset (16k lines, 4% in the minority class), using random forest to for a binary classification. I’m using the Python Sklearn implementation of ...
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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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147 views

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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178 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 ...
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256 views

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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45 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 ...
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1answer
135 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 ...
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459 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 ...
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3answers
551 views

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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31 views

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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121 views

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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213 views

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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1k views

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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245 views

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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5k views

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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1answer
107 views

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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209 views

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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336 views

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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2answers
314 views

Use of SMOTE with training, test and dev sets

I have a dataset with class imbalance about (1:10). I applied a SMOTE method by slitting the dataset into training and dev. I over sampled the training set using a SMOTE method and divided this set ...
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89 views

Bias-Variance Tradeoff when using Oversampling Technique

Oversampling techniques (e.g. SMOTE) are often used when target values are not approximately equally represented. How does this technique affect bias and variance of the predictive model that is ...
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1k views

SMOTE data balance - before or during Cross-Validation

I'm using Random Forest in the CARET package to tag a binary outcome with 1/10 ratio, thus I need to balance the dataset. I know two ways: Use SMOTE as a stand-alone function and then pass it to the ...
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529 views

random forest imbalanced data-over, under, Smote Sampling

I am using random forest model for an imbalanced dataset. The dependent variable is Yes=73, No=7100. I have 65 independent variables both factor and numeric. I have tried to develop models for ...
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301 views

Applying SMOTE and increasing sensitivity

I am trying to analyze lending club data and want to predict whether a loan is risky or safe using random forest with decision tree as a classifier. The data is imbalanced. It contains one-fourth of ...
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14k views

ROSE and SMOTE oversampling methods

Can somebody give me a brief explanation of the differences between those two resampling methods : ROSE and SMOTE ?