Data Science

Overfitting and Underfitting models in Machine Learning

In most of our posts about machine learning, we've talked about overfitting and underfitting. But most of us don't yet know what those two terms mean. What does it acutally mean when a model is overfit, or underfit? Why are they considered not good? And how do they affect the accuracy of our model's predictions? These are some of the basic, but important questions we need to ask and get answers to. So let's discuss these two today. The datasets we use for training and testing our models play a huge role in the efficiency of our models. Its equally important to understand the data we're working with. The quantity and the quality of the data also matter, obviously. When the data is too less in the training phase, the models may fail to understand the patterns in the data, or fa...

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