Questions tagged [smote]

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

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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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30 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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188 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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77 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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479 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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6 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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11 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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15 views

Learning Curve with SMOTE

I want to be able to plot the learning curve of my model. However, I want to see how the model is learning when I include SMOTE. sklearns learning curve implements internally cross validation, and ...
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1answer
45 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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57 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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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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452 views

SMOTE sampling for multi-class data

I have a classification problem with 4 distinct classes, but imbalanced data. ...
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95 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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136 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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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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274 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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282 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 ...