![]() ![]() Hence, the underfitting and overfitting are the two terms that need to be checked for the performance of the model and whether the model is generalizing well or not.īefore understanding the overfitting and underfitting, let's understand some basic term that will help to understand this topic well: It means after providing training on the dataset, it can produce reliable and accurate output. ![]() ![]() ![]() Here generalization defines the ability of an ML model to provide a suitable output by adapting the given set of unknown input. The main goal of each machine learning model is to generalize well. Overfitting and Underfitting are the two main problems that occur in machine learning and degrade the performance of the machine learning models. Next → ← prev Overfitting and Underfitting in Machine Learning
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