{"id":352,"date":"2023-12-29T15:08:23","date_gmt":"2023-12-29T06:08:23","guid":{"rendered":"https:\/\/datalab.flitto.com\/en\/company\/blog\/?p=352"},"modified":"2024-01-11T11:21:54","modified_gmt":"2024-01-11T02:21:54","slug":"what-is-underfitting-and-overfitting-easy-example","status":"publish","type":"post","link":"https:\/\/datalab.flitto.com\/en\/company\/blog\/what-is-underfitting-and-overfitting-easy-example\/","title":{"rendered":"What is Underfitting and Overfitting? (Easy Example)"},"content":{"rendered":"\n
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If the concept of underfitting and overfitting confuses you, you’re not alone. (Trust us!)<\/p>\n\n\n\n

Navigating machine learning terminologies can be very difficult, especially if you’ve only recently started to dig deeper.<\/p>\n\n\n\n

The good news is that, as a non-expert, it’s still possible to have a grasp on what each term means using simplified examples. And this is especially so if your goal is to just familiarize yourself with some key concepts in machine learning, so that you can better appreciate the perks of AI systems.<\/p>\n\n\n\n

In this article, we’ll talk about the concepts of underfitting and overfitting in machine learning. If you’ve kept a keen eye on the field, you may have heard at least one of the two. These two concepts continue to plague machine learning and data professionals, and that is perhaps why they are commonly thrown about in the field.<\/p>\n\n\n\n

Let’s have a look at what underfitting and overfitting are, together with some simplified examples.<\/p>\n\n\n\n

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What is underfitting and overfitting in machine learning?<\/h2>\n\n\n\n
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Underfitting and overfitting refer to a set of negative phenomena in machine learning that interfere with optimal model performance. They are often caused by a mismatch between a machine learning model and the data it’s trained on.<\/p>\n\n\n\n

Even if you’re new to the term, you’ve probably already experienced minor cases of underfitting and overfitting. For instance, a voice assistant not being able to understand some voice commands at all due to certain accents can be a result of overfitting. Meanwhile, the infamous AI hallucinations can be caused by underfitting.<\/p>\n\n\n\n

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Performance without underfitting and overfitting<\/h2>\n\n\n\n
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An ideal or good fit machine learning model has a good balance<\/a> between capturing the patterns within the training data and generalizing well to new, unseen data. It can capture the underlying patterns and relationships present in the training data, without memorizing things word-by-word or detail-by-detail.<\/p>\n\n\n\n

The model should also perform well on new, unseen data that wasn’t part of the training set. It is able to demonstrate an understanding of the underlying concepts, rather than overly relying on specific details.<\/p>\n\n\n\n

A good fit model has a clearly defined scope and a dataset that clearly aligns with it. As a result, its performance might look like this.<\/p>\n\n\n\n

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A tiger recognition model with a right fit<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n
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What is underfitting in machine learning?<\/h2>\n\n\n\n
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Underfitting occurs when a model is too simple<\/strong> to understand the patterns in the data. <\/p>\n\n\n\n

An underfit model doesn’t learn the data well and may perform poorly on both the training set and new, unseen data because it oversimplifies<\/strong> the relationships in the data. Imagine a shoe that can fit anything between a human foot and a little dog because it’s too big and loose (underfitting).<\/p>\n\n\n\n

Several factors can contribute to underfitting. It can be caused by a lack of, or limited data.<\/p>\n\n\n\n

Let’s look at the performance of an underfit tiger recognition model.<\/p>\n\n\n\n

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An underfit tiger recognition model<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n
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In the visual example above, for instance, the model may have been built to define tiger with a single feature: <\/p>\n\n\n\n