21 questions linked to/from What problem does oversampling, undersampling, and SMOTE solve?
Why over/under-sampling could not help my model fitting? [duplicate]
I fit random forest to my imbalanced dataset with minority class 1. I found that the AUC under the imbalanced data was better than that of re-sampled dataset (over/under sampling). Can someone help to ...
When is unbalanced data really a problem in Machine Learning?
We already had multiple questions about unbalanced data when using logistic regression, SVM, decision trees, bagging and a number of other similar questions, what makes it a very popular topic! ...
Binary classification with strongly unbalanced classes
I have a data set in the form of (features, binary output 0 or 1), but 1 happens pretty rarely, so just by always predicting 0, I get accuracy between 70% and 90% (depending on the particular data I ...
Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
TL;DR See title. Motivation I am hoping for a canonical answer along the lines of "(1) No, (2) Not applicable, because (1)", which we can use to close many wrong questions about unbalanced ...
Removing duplicates before train test split
Let's say you have a dataset generated from real world sampling which has lots of duplicates (the dependent and independent variables are identical) and you want to train a classifier to predict the ...
random forest for imbalanced data?
I have a dataset where yes=77 and no=16000, a highly imbalanced dataset. My plan was to identify the most important variables influencing the response variable using random forest and then develop a ...
When is oversampling poor practice?
For my particular domain and problem, I have data on the entire population. However, my "event" only occurs in 0.5% of the cases. I want my model to be able to pick up on significant characteristics ...
Imbalanced Test Data
I have an imbalanced (1:5) training and test set with only two classes and have oversampled the training set with SMOTE so that the class ratio is 1:1. The ML model gives values over 0.7 for accuracy, ...
How do I perform a logistic regression w/ SMOTE
I want to understand which variables lead to an infection by parasites in a tree. Hence, I want to use stepwise logistic regression based on AIC. First, I describe what I would do, and then my code ...
Effects of class imbalance on nn batch training
Say I have a binary classification task, where the positive class (1) is only 1% of the whole data set. Intuitively I can understand why this could be bad for the classifier as the model may learn ...
Why would random forest perform bad on unbalanced class
There is a huge number of posts saying that an imbalanced classes are bad. And only half explains it in terms of recall-presicion scores, meaning that accuracy can be high but F1 score low. What I ...
Prediction for imbalanced and small sample sized data
I have to create a classification model where my dataset contains 697 observations which only 18 are from the group of interest. As usual, I split data the into a training and test set stratified by ...
Imbalance Classification : How SMOTE handles majority class?
I'm working on Imbalance Classification problem with minority class(0.017%). I've read that imbalance classification can be handled using Undersampling, Oversampling and SMOTE. Major drawback of ...
What are other ways of doing oversampling apart from SMOTE?
I have just begun learning about machine learning techniques and started solving problems on kaggle. I have a few questions about how to handle class imbalance: How to handle imbalance dataset ...
Neural network for imbalanced data
I have an imbalanced data (n = 600, about 97% majority and 3% minority) with 20 features and a binary outcome. The data has been split into a training set and a test set (80%/20%). I used H2o autoML ...