Underfitting

Underfitting is a term used in data analytics to describe a model that’s too simple to capture the complexity of the data it’s trying to predict or understand. It’s like trying to understand a whole story by only reading one page. In business intelligence, this is a problem because it means the model isn’t accurate enough to be useful. It can’t make good predictions or give insights that are reliable because it hasn’t learned enough from the data it was trained on.

Think of underfitting as the opposite of getting a perfect fit. If you have a pair of shoes that are too small, they don’t fit your feet’s contours and are uncomfortable. Similarly, with underfitting, the data model doesn’t match the intricacies of the real-world data. It oversimplifies things, missing important trends or patterns. For businesses, this could lead to making decisions based on flawed insights, which might not work out well because they’re based on a model that doesn’t understand the full picture.

To avoid underfitting, data scientists need to use more complex models or additional features that can capture the behavior of the data more accurately. They also need to make sure they have enough quality data to train the model. By addressing underfitting, businesses can create more accurate and effective models that help them understand their customers and markets better, leading to smarter business strategies.