Linked Questions

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23 views

Risks of disproportionate numbers of training examples for different classes? [duplicate]

fairly new to machine learning so please be gentle! I was wondering what the risks might be if I have significantly more examples for one class than the other when training a 2-D perceptron; for ...
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0answers
894 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 ...
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0answers
148 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 ...
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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 ...
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0answers
9 views

Unbalanced classification problem [duplicate]

I am trying to build models for the KDD cup 2004 challenge. The protein homology problem data is divided into several blocks with roughly 1000 samples in it. Each block has an unbalanced class problem....
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22 views

Is matching necessary for prediction from logistic regression?

I have a data set with very imbalanced groups (about 3% have an effect, while 97% don't). I experimented with logistic regression and was convinced that I can get to a very good accuracy of prediction ...
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0answers
116 views

Balanced LogLoss with XGBoost

Following the discussion on here I started worrying less about class imbalance. However, I recently started building a predictor, using XGBoost, and I wanted to used LogLoss as my target metric. I ...
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0answers
18 views

At which threshold is unbalanced data a problem for a binary classification tree? [duplicate]

I want to build a binary classification tree to clasfiy wether a person is working or not and use the model for prediction. I read that unbalanced data could be a problem. Now i ask myself at which ...
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10 views

Do I need to treat class imbalance before preforming RFE (Recursive Feature Elimination)? [duplicate]

I am trying to perform RFE on an imbalanced data set that will later be used for binary classification. I have chosen to use the caret::rfe package for feature selection. I am using random forests ...
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48 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 ...
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9 views

I am building model for classification and in practice my data could be imbalance to any extend. How could I build single ml model to perform task? [duplicate]

for example: Case 1: Class A:10 Observations Class B: 100 Observation Case2: Class A: 100 Observations Class B: 10 Observations How could I build a single model (Random Forest) to perform this task?
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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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0answers
16 views

Will more training data of one class destroy the good model? [duplicate]

I am facing a binary classification problem that I don't know I should use more data or not. I have one label 'A' with 10 training examples. And another label 'B' with also 10 training examples. By ...
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0answers
36 views

Training set target categories' distribution [duplicate]

In a book I'm reading I've come across the following quote: Accuracy on the test set is a good performance measure only when there is a relatively uniform distribution of target categories in the ...
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0answers
34 views

unbalaced data set in classification tasks [duplicate]

How do I judge whether the dataset is unbalanced (is it when the minority class inferior of 15%) could us use the test CR after balancing the data?

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