{"id":258,"date":"2020-08-10T18:11:12","date_gmt":"2020-08-10T18:11:12","guid":{"rendered":"https:\/\/data-science.gotoauthority.com\/2020\/08\/10\/best-ways-to-handle-imbalanced-data-in-machine-learning\/"},"modified":"2020-08-10T18:11:12","modified_gmt":"2020-08-10T18:11:12","slug":"best-ways-to-handle-imbalanced-data-in-machine-learning","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2020\/08\/10\/best-ways-to-handle-imbalanced-data-in-machine-learning\/","title":{"rendered":"Best Ways To Handle Imbalanced Data In Machine Learning"},"content":{"rendered":"
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When dealing with any classification<\/a> problem, we might not always get the target ratio in an equal manner. There will be situation where you will get data that was very imbalanced, i.e., not equal. <\/strong>In machine learning world we call this as class imbalanced data issue.<\/p>\n

Building models<\/a> for the balanced target data is more comfortable than handling imbalanced data; even the classification algorithms find it easier<\/strong> to learn from properly balanced data.\u00a0<\/p>\n

But in real-world, the data is not always fruitful to build models easily. We need to handle unstructured<\/strong> data, and we need to handle imbalance<\/strong> data.<\/p>\n

So as a data scientist or analyst, you need to know how to deal with class imbalance.<\/p>\n

In this article, we are going to give insights about how to deal with this situation. There are various techniques used to handle imbalance data. Let’s learn about them in detail along with implementation in python.<\/p>\n<\/div>\n

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Best way to handle imbalanced data in machine learning<\/p>\n<\/div>\n

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Before we go further, Let’s look at the topics you will learn by the end of this article.<\/p>\n<\/div>\n

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What is class Imbalance in machine learning?<\/h2>\n

In machine learning class imbalance is the issue of target class<\/strong> distribution. Will explain why we are saying it is an issue. If the target classes are not equally distributed or not in an equal ratio, we call the data having an imbalance data issue.<\/p>\n

Examples of balanced and imbalanced datasets<\/h3>\n

Let me give an example of a target class balanced and imbalanced datasets, which helps in understanding about class imbalance datasets.<\/p>\n

Balanced datasets:-<\/h4>\n