Linked Questions
329 questions linked to/from Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
30
votes
3
answers
23k
views
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 ...
- 351
1
vote
2
answers
832
views
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 ...
- 11
0
votes
1
answer
188
views
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 ...
- 43
0
votes
0
answers
174
views
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 ...
- 1
0
votes
1
answer
71
views
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, ~...
- 1
0
votes
0
answers
120
views
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 ...
- 63
0
votes
1
answer
61
views
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 ...
- 103
0
votes
1
answer
44
views
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 ...
0
votes
0
answers
52
views
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 ...
- 689
2
votes
0
answers
42
views
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 ...
- 51
1
vote
0
answers
27
views
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 ...
- 11
0
votes
0
answers
17
views
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?
- 65
0
votes
0
answers
10
views
Imbalanced dataset with multiple classes [duplicate]
I have an imbalanced dataset with multiple classes where some have less than 100 some are more than 10k,where i want to apply random forest(the dataset is confidential so i cant share),i used all ...
0
votes
0
answers
8
views
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.
...
- 239
245
votes
11
answers
136k
views
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.
...
- 140k