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

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SMOTE algorithm in R

I have a couple of doubts regarding SMOTE in R using the package DMwR. I am getting new attributes, one without any name and another named as 'X'. These two are completely new after applying SMOTE ...
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50 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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SMOTE in Bayesian Networks

Oversampling or SMOTE is useful when the data is imbalanced. Here is the question I cant find the answer: Since we are dealing with probabilities in Bayesian Networks (probabilistic graphical model), ...
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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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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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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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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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22 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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334 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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335 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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64 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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282 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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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 ...