Linked Questions

4
votes
4answers
2k views

How to handle data imbalance in classification? [duplicate]

I am working on a text classification problem. My data is highly imbalanced. For example, one category has 700 documents while the other has 30. I have around 30 categories. I tried different ...
1
vote
0answers
2k views

Repeating rare examples in unbalanced data classification [duplicate]

So I'm trying to train a neural network for a rare event detection. Based on that, I have like 1000 times more examples for non-target (everything else) examples that I have for target examples. So I ...
22
votes
0answers
459 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
2answers
572 views

Machine Learning with Skewed Classes in R [duplicate]

I am looking for some suggestions on what methods are appropriate for training a dataset with a high skew in the outcome classes. The ratio of Class 0: Class 1 is about 20:1 and I am looking to ...
0
votes
1answer
616 views

Does it make sense to up-sample imbalanced data if I care about predicting the right probabilities? [duplicate]

I'm developing a predictive model on a dataset of about 25K observations with the response variable having ~60 classes. There are ~130 predictor variables, all of them binary. In this problem I care ...
7
votes
2answers
260 views

Should we balance the data set if the data is intrinsically unbalanced? [duplicate]

Say I want to predict the cancer rate(regression)/predict the whether a person has cancer or not(classification). The data intrinsically has few cancer patients/low cancer rate, say 1/200. And the ...
2
votes
1answer
234 views

When do we speak of imbalanced data? [duplicate]

I was wondering, what the threshold is for a dataset to be called 'imbalanced'? Technically every dataset where the target-classes aren't evenly distributed is not balanced. Yet small imbalance ...
1
vote
0answers
283 views

Classification model on a highly unbalanced dataset [duplicate]

I’m dealing with a highly unbalanced dataset where 20% of data belongs to class A and 80% belongs to class B. It’s very hard for us to produce synthetic class A data. Just wondering if the below ...
2
votes
2answers
89 views

Does the ratio of training data (significantly) affects the result? [duplicate]

Say I have two training data of email titles with 10000 entries each. One of them have 2000 ham and 8000 spam, while the other have 5000 ham and 5000 spam. Will predicting any random email with the 2:...
2
votes
0answers
233 views

Why imbalance in the data set is an issue in data mining and machine learning? [duplicate]

I've analyzed a data set from a credit card company before and it has the famous "unbalanced classes" problem like all the other credit card companies, i.e., in the data set (the information of users ...
1
vote
3answers
177 views

Binary classification with imbalanced classes [duplicate]

I have a manufacture dataset of 65 million rows corresponding to 65 millions distinct items. Out of those 65 millions, I have 60,000 of them that failed a certain test, thus I have very imbalanced ...
1
vote
0answers
170 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 ...
0
votes
0answers
70 views

Handling imbalanced data for classification [duplicate]

What are the best ways to deal with imbalanced datasets for classifying whether or not individuals pay their tuition? The data is 75% positive class (paid) and 25% negative (unpaid). Some approaches I ...
1
vote
0answers
67 views

While dealing with imbalanced classes, to what extent can we upsample a minority class? [duplicate]

I have my training data with the following approximate distribution: Negative events : 90,000 positive events : 5,000 Training a model would require to oversample the minority class (and might also ...
0
votes
0answers
45 views

Choosing better samples for downsampling [duplicate]

I'm training a classification model for highly unbalanced data where hits are pairs that have similarity of almost 1 on independent metric. Everything else are non hits. Does it make sense to pick ...

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