Tagged Questions
1
vote
0answers
16 views
Problem with classifier after using SMOTE to balance the data
We've ran into a problem while training a classifier on an unbalanced data set.
The response is binary with 0 indicating 'non defaulter' and 1 indicating 'defaulter' (it's a credit scoring task).
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2
votes
1answer
75 views
Which performance measure for unbalanced binary classification without an 'active' class?
My datasets have two classes A and B. The classes should be treated equally (there is no "active/inactive"). The datasets are unbalanced, sometimes A is more frequent, sometimes B is more frequent. ...
2
votes
0answers
75 views
How to use k nearest neighbours for binary classification with unbalanced classes?
I have relatively large (100k items) dataset which I need to split in two groups. So far I've tried knn and the results are not good mainly because I have disproportion in my training data: 90% of ...
8
votes
1answer
127 views
How to handle the difference between the distribution of the test set and the training set?
I think one basic assumption of machine learning or parameter estimation is that the unseen data come from the same distribution as the training set. However, in some practical cases, the distribution ...
6
votes
1answer
234 views
CART (rpart) balanced vs. unbalanced dataset
I am fitting a tree (CART) to the olives-dataset. The training data has 436 observations (test data: 136). I have 3 responses (the 'Region' variable) which splits the training data into 116 / 74 / 246 ...
3
votes
3answers
734 views
SVM vs. artificial neural network
I have multiclass unbalanced data (4 class with 15% 25% 45% 15% data in each class). Which method is good for classification of such data- SVM or ANN?
UPDATE- Let me make the question little more ...
6
votes
1answer
484 views
When over/under-sampling unbalanced classes, does maximizing accuracy differ from minimizing misclassification costs?
First of all, I would like to describe some common layouts that Data Mining books use explaining how to deal with Unbalanced Datasets. Usually the main section is named Unbalanced Datasets and they ...
