Linked Questions

30 votes
3 answers

Testing Classification on Oversampled Imbalance Data [duplicate]

I am working on severely imbalanced data. In literature, several methods are used to re-balance the data using re-sampling (over- or under-sampling). Two good approaches are: SMOTE: Synthetic ...
Fares's user avatar
  • 351
1 vote
2 answers

Classification with imbalanced data [duplicate]

I have a dataset that is highly imbalanced. I did some research on Internet, however I did not find what I was looking for. What is the correct sequence for dealing with imbalanced data? Should we ...
Bengu Atici's user avatar
0 votes
1 answer

loss function for probability maps [duplicate]

I am applying a deep learning architecture to predict the spatial distribution of nightly thunderstorms over location B based on the thunderstorms of the preceding afternoon over location A. Their ...
Seppe Lampe's user avatar
0 votes
0 answers

Explanatory analysis Logistic Regression with unbalanced data [duplicate]

I have a dataset that looks at the default and non-default of companies. Thus it contains one variable 'company default' that is 1 or 0 and some extra descriptive variables. However my dataset is ...
cmrn's user avatar
  • 1
0 votes
1 answer

Proving a class imbalance IS a problem in Machine Learning [duplicate]

Context: Have been trying to create a prediction model for a 1% outcome variable using Random Forest Machine Learning for a large health survey (entirely multi-level categorical data, yes/no outcome, ~...
MJay's user avatar
  • 1
0 votes
0 answers

Precision and Recall are very less [duplicate]

The model has a problem in predicting since the data was not balanced first I used SMOTE for balancing. I have used some other techniques to balance the data like under sampling and performing upper ...
10sha25's user avatar
  • 63
0 votes
0 answers

Target variable has 99.6% of data with same class and 0.4% of data of another class [duplicate]

For my Credit card fraud detection (classification) project I have got two data set, Train.csv and Test.csv When I checked the summary of the dataset I found my target variable, "is_fraud" ...
Pawan Kumar's user avatar
0 votes
1 answer

Expected unbalanced-classes [duplicate]

I want to train a binary classifier but I have an expected unbalanced dataset (90%/10%). Should I train an unbalanced dataset or to downsample the first class? This ratio is natural and will be always ...
snowflake's user avatar
  • 103
0 votes
1 answer

In which specific situations is minority class oversampling useful? [duplicate]

I understand that, in the context of a binary classification problem, downsampling the majority class is a useful strategy to come up with a smaller, computationally friendly dataset. Using this ...
Jose Manuel Albornoz's user avatar
0 votes
0 answers

SMOTE isn't helping with logistic regression reults - for imbalanced data [duplicate]

I have a dataset that is highly imbalanced, talking about 230 cases of class 1 in the target feature, and more than 3800 of class ...
Programming Noob's user avatar
2 votes
0 answers

Resampling to handle class imbalance in logistic regression [duplicate]

I was wondering if anyone could help me understand resampling for class imbalance. From what I have learned, class imbalance is usually a small data problem where the less prevalent class usually ...
dzheng1887's user avatar
1 vote
0 answers

Is there actually a right and wrong way to deal with major imbalance in logistic regression (or other models, really)? [duplicate]

I have seen a lot of different advice on how to deal with imbalance, and I get that it can be case-specific. But I learned in school that SMOTE oversampling or undersampling were basically the ways ...
Siri C's user avatar
  • 11
0 votes
0 answers

Is there any point upsampling a minority class if it is 40% of the dataset? [duplicate]

The minority class of my target variable is 40% of the dataset. Is there any point to upsampling them to 50%? or is upsampling only used when there is severe class imbalance?
ibarbo's user avatar
  • 23
0 votes
0 answers

Object detection: better to train with imbalanced dataset or remove images to balance out [duplicate]

I am training an object detection model using the YOLOv5 architecture. I have the following classes and counts. ...
Peter's user avatar
  • 239
232 votes
11 answers

Why is accuracy not the best measure for assessing classification models?

This is a general question that was asked indirectly multiple times in here, but it lacks a single authoritative answer. It would be great to have a detailed answer to this for the reference. ...
Tim's user avatar
  • 136k

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