From the course: Mistakes to Avoid in Machine Learning

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Not treating for imbalanced sampling

Not treating for imbalanced sampling

From the course: Mistakes to Avoid in Machine Learning

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Not treating for imbalanced sampling

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- [Brett] In machine learning classification, you'll often encounter problems where the target variable you're trying to predict occurs very infrequently. And as a result, it can be very difficult to develop a good prediction. This is a problem of an imbalanced distribution and many machine learning techniques struggled to produce meaningful results. So imbalanced data is something you encounter in a classification problem in which the number of observations per class are disproportionately distributed. These problems can be difficult to solve, but luckily, there are several sampling techniques that you can leverage to improve your results. These problems can be difficult to solve, but luckily, there are several sampling techniques that you can leverage to improve your results. Introducing the imbalanced learn, imb-learn package. This is an excellent package which offers a variety of sampling techniques for dealing with…

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