Linked Questions

1
vote
0answers
141 views

positive and negative sample count for ConvNets [duplicate]

I have been trying to set up a ConvNet to classify some data. This data should be classified to either 1 (being what I need to get from the image) and 0 for everything that is irrelevant. I have ...
92
votes
7answers
26k 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. ...
45
votes
5answers
8k views

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! ...
6
votes
1answer
3k views

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 ...
2
votes
1answer
647 views

Purpose of class balancing

I see people doing class balancing (via oversampling, etc.) before learning classifiers all the time. I wanted to know why does class balancing improve classification accuracy. Is it true all the time....
1
vote
0answers
851 views

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 ...
15
votes
0answers
275 views

Are unbalanced datasets problematic, and (how) does oversampling (purport to) help? [duplicate]

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 datasets ...
0
votes
1answer
157 views

classification in imbalanced datasets: how to measure performance on test set?

I am using re-sampling methods to address the imbalance between classes for my binary classification problem. I am not sure how to measure the performance of my model on the test set: should I re-...
1
vote
0answers
153 views

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 ...
2
votes
0answers
59 views

What are the recommended over-sampling or under-sampling techniques to handle multi-class imbalanced datasets for machine learning? [duplicate]

Many techniques are for two-class classification. What is the recommended techniques to over- or under-sample multi-class datasets?