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In Machine Learning, an error is an important measure of how accurately our model can predict on data that it uses to learn as well as how it behaves with new and unseen data. Based on the error, we tend to choose the machine learning model which would produce the best performance on a particular dataset. In this article, we will be discussing one such problem in machine learning models known as the Bias-Variance Tradeoff. We will try to explore what it is and how one can overcome it. Below is the outline of important points that we will cover in this article.