All Questions
6 questions
5
votes
1
answer
587
views
Are imbalanced data problems solvable? [closed]
I am working as a data scientist for the past 2 years where I have worked on problems related to binary classification, revenue prediction etc.
In the past two years, I have had 2 problems that ...
- 2,655
1
vote
1
answer
31
views
Remedie for a stubborn recall result?
I was working on a project connected to predicting default on credit loan with 0-1 loss. The recall is a crucial measure that should be maximized in this case, while monitoring precision for sanity of ...
8
votes
2
answers
110
views
Which classification algorithms are negatively affected by class imbalances?
I've seen a few posts and papers floating around the web (mostly those related to over/undersampling, SMOTE, and cost-sensitive training) that, when discussing class imbalance, specify that certain ...
0
votes
2
answers
478
views
Can we make two separate models vs one for classification?
Suppose I have a binary classification problem and my data is imbalanced, I can build a classification model using any of the algorithms and use an oversampling or undersampling technique to handle ...
2
votes
1
answer
8k
views
How to improve precision under imbalanced classification
I am using an imbalanced dataset (rare positive cases) to learn models for prediction and the final good AUC is 0.92 but the F1 score is very low0.2.
Is it possible to add some key features which ...
- 783
-1
votes
1
answer
33
views
Time-based over-sampling dilemma
Background:
I'm working on a binary classifier that tries to predict when -- if ever -- a user goes bad, a terminal state from which a user cannot recover. This phenomenon is tricky becuase a user ...
- 4,189