Questions tagged [smote]

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

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6
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1answer
11k views

ROSE and SMOTE oversampling methods

Can somebody give me a brief explanation of the differences between those two resampling methods : ROSE and SMOTE ?
5
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1answer
154 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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0answers
171 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 ...
3
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1answer
955 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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1answer
13 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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0answers
5 views

How to implement smogn(smote for regression) in python? [on hold]

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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3answers
187 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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2answers
144 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 ...
1
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0answers
70 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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0answers
452 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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2answers
255 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 ...
0
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1answer
724 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 ...
0
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1answer
129 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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1answer
2k 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 ...
0
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1answer
85 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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0answers
25 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 ...
0
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1answer
25 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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0answers
43 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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0answers
19 views

Interpreting results of a class-balanced model?

I'm working on a logistic regression model in order to model a relationship and am facing a class-imbalance problem (way too many 0's and not enough 1's). In order to resolve this, I'm planning to use ...
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0answers
357 views

SMOTE sampling for multi-class data

I have a classification problem with 4 distinct classes, but imbalanced data. ...
0
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0answers
83 views

SMOTE algorithm gives better AUC than matching

I have a highly imbalanced data set: a total of 13000 patients, 160 having condition A, and various other features which could be predictors. In order to balance the data I did two things: 1) ...
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0answers
114 views

Can SMOTE Be Used for Pure Data Augmentation and not Just Imblanaced Classes

I have learned that SMOTE can be used to deal with imbalanced class datasets. Could it also be used to create a larger dataset, preserving the original structure/distribution and thus also the ...
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0answers
12 views

For ADASYN, if the neighbourhood of a minority sample contains no other minority sample, do I double the sample?

In ADASYN, for the last step in the paper linked below, if there exists no other minority class in the k-NN other than the one minority example, do we simply just double the training example? Because ...
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0answers
261 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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0answers
270 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 ...