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Questions tagged [smote]

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

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results of k means clustering model did not change much after using SMOTE

I have 2 classes that needs to be classified. As my dataset is very large (about 8 mil data points), I first did a random undersampling then used K-means clustering. This is my result: ...
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7 views

Balancing independent variable and model performance(using SMOTE)

I have a data set which contains 7 independent variables and one dependent variable. I tried applying some of the classification algorithms to predict the binary target variable. I got about 96% ...
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1answer
45 views

Reverse CARET proProcess()

I did the following steps in my modeling using R: 1)applied preProcess(data, method = c("bagImpute")) function in CARET package and then encoded the data. 2)Used SMOTE to balance the data(because the ...
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1answer
57 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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1answer
30 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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45 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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17 views

Resampling imbalanced data in a multi-view scenario

Assume a multi-view scenario, where multiple views of the same entity are available. If each data pair is assigned a label and the resulting scenario is highly imbalanced, what are proper ways of ...
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1answer
19 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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28 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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1answer
462 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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459 views

Why does SMOTE over-sampling improve my accuracy? [closed]

I have an imbalanced dataset, for which I first split my data into train and test and then apply SMOTE on train data only. When I run the model on original test data (my test set is also imbalanced), ...
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88 views

SMOTE for binary features in R

I have a classification problem with 1330 observations and 139000 variables. All the variables are binary(0,1). The data is imbalanced with 97.8 % 0's and 2.2% 1's.I tried using SMOTE to balance that ...
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326 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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105 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 ...