- Published on October 29, 2020
- In Deep Tech
Using Near-Miss Algorithm For Imbalanced Datasets
In this article, we will learn about the near-miss algorithm, the different versions of it and implement the different versions on an imbalanced dataset.
- By Bhoomika Madhukar
Consider a scenario where you have to classify between apples and oranges and 90% of your dataset contains apples. This leaves only 10% of the data to oranges and the model tends to get biased towards apples. This type of dataset is called an imbalanced dataset and affects the performance of the model. To overcome this, the near-miss algorithm can be applied to the dataset.
In this article, we will learn about the near-miss algorithm, the different versions of it and implement the different versions on an imbalanced dataset.
What is the Near-Miss Algorithm?
Near-miss is an algorithm that can help in balancing an imbalanced dataset. It can be grouped under undersampling algorithms and is an efficient way to balance the data. The algorithm does this by looking at the class d
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Bhoomika Madhukar
I am an aspiring data scientist with a passion for teaching. I am a computer science graduate from Dayananda Sagar Institute. I have experience in building models in deep learning and reinforcement learning. My goal is to use AI in the field of education to make learning meaningful for everyone.
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