What are the solutions for class imbalance problem?. For example, we have millions of positive examples and hundreds of negative examples. What are all the approaches for building binary classifier?
There are various ways to handle imbalanced dataset, frequently used are
Oversampling
Undersampling
Hybrid
The link might be useful.
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I am working on a machine learning project and my data is imbalanced, with one class having a much smaller number of examples than the other. This has led to biased models and poor predictive performance, especially for my target variable which is of high importance. I have already tried resampling techniques such as oversampling, undersampling, and both, but I'm still struggling to achieve the desired performance.
One possible solution from paulduf for handling class imbalance is to use a package that is specifically designed for this purpose. They mentioned using the 'imbalanced-learn' package, which is compatible with scikit-learn, has a lot of stars and looks up-to-date/maintained. I looked at the package and it does provide a variety of methods for dealing with imbalanced datasets, such as sampling techniques, cost-sensitive learning, and ensemble methods. Here's the link to the package: github.com/scikit-learn-contrib/imbalanced-learn imbalanced-learn.org/stable/user_guide.html Thank you paulduf.
Aside from using a package, what are some other effective tips and best practices for handling class imbalance in machine learning that have been shown to work in real-world scenarios? Are there any specific methods that have been proven to be successful in addressing this problem, such as anomaly detection techniques? Any help or guidance would be greatly appreciated, as I am looking to improve the performance of my machine learning models in the context of imbalanced datasets.
I have been working on the case study where data is highly imbalanced. we have been taught we can handle the imbalanced data by either under sampling the majority class or over sampling the minority class.
I wanted to ask if there is any other way/method that can be used to handle imbalanced data?
this question is more on conceptual side than programming.
for example,
I was thinking if we could put some weight on minority class (conceptually) to make the model emphasize on identifying pattern in minority class.
I don't know how that can be done but this concept theoretically should work.
feel free to put crazy ideas too.
Your weights idea is not far off. This can be done. In fact, most sklearn Models give you the option to specify class weights. This however is often not enough for very extreme cases (e.g. 95%/5% split or more extreme).
There are specific oversampling techniques such as SMOTE (and related techniques) which go a step further than classic oversampling and generate synthetic samples based on a K Nearest Neighbors Algorithm.
If the classes are extremely imbalanced a "classic" classification approach may not be enough and you might have to look into anomaly detection algorithms.
Just use strong consistent classifier. See https://arxiv.org/abs/2201.08528
Generally speaking I think that you need, before diving into the technical solution (under/upsampling, smote ..) consider the business KPI you are predicting and whether there is a proxy that could help reduce the disparity rate between the classes.
You can think also about the models that have weights parameters and could penalize the majority class
You can check this article, it explains from a conceptual point of view how to deal with imbalanced data in general.
I am trying to model a classifier using XGBoost on a highly imbalanced data-set, with a limited number of positive samples and practically infinite number of negative samples.
Is it possible that having too many negative samples (making the data-set even more imbalanced) will weaken the model's predictive power? Is there a reason to limit the number of negative samples aside from running time?
I am aware of the scale_pos_weight parameter which should address the issue but my intuition says even this method has its limits.
To answer your question directly: adding more negative examples will likely decrease the decision power of the trained classifier. For the negative class choose the most representative examples and discard the rest.
Learning from imbalanced dataset can influence the predictive power and even an ability of a classifier to converge at all. Generally recommended strategy is to maintain similar sizes of training examples per each of the classes. Imbalance of classes effect on learning depends on the shape of the decision space and the width of boundaries between classes. The wider they are, and the simpler the decision space the more successful training even for imbalanced datasets.
TL;DR
For a quick overview of the methods of imbalanced learning I recommend these two articles:
SMOTE and AdaSyn by example
How to Handle Imbalanced Data: An Overview
Dealing with Imbalanced Classes in Machine Learning
Learning from Imbalanced Data by Prof. Haibo He (more scientific)
There is a Python package called imbalanced-learn which has an extensive documentation of algorithms that I recommend for in-depth review.
I am intended to do a yes/no classifier. The problem is that the data does not come from me, so I have to work with what I have been given. I have around 150 samples, each sample contains 3 features, these features are continuous numeric variables. I know the dataset is quite small. I would like to make you two questions:
A) What would be the best machine learning algorithm for this? SVM? a neural network? All that I have read seems to require a big dataset.
B)I could make the dataset a little bit bigger by adding some samples that do not contain all the features, only one or two. I have read that you can use sparse vectors in this case, is this possible with every machine learning algorithm? (I have seen them in SVM)
Thanks a lot for your help!!!
My recommendation is to use a simple and straightforward algorithm, like decision tree or logistic regression, although, the ones you refer to should work equally well.
The dataset size shouldn't be a problem, given that you have far more samples than variables. But having more data always helps.
Naive Bayes is a good choice for a situation when there are few training examples. When compared to logistic regression, it was shown by Ng and Jordan that Naive Bayes converges towards its optimum performance faster with fewer training examples. (See section 4 of this book chapter.) Informally speaking, Naive Bayes models a joint probability distribution that performs better in this situation.
Do not use a decision tree in this situation. Decision trees have a tendency to overfit, a problem that is exacerbated when you have little training data.
If I have two very different datasets and two very different classification techniques, is there a good way to combine the two outputs? I understand an average may work but is there a more relevant way to do this? I've heard of several concepts like boosting and ensemble learning, would these be applicable?
There are two general ways to go about this problem. The first, called boosting, uses weighted voting to decide on the prediction. The main idea is to combine advantages of both classifiers.
The second approach, called stacking, uses the outputs of the two classifiers as features into another classifier (possibly with other features, e.g. the original ones), and use the output of the final classifier for the prediction.
In the absence of further details, this is the best answer I can give.
See Bagging, boosting and stacking in machine learning on Stats.SE for more.