What is Overconfidence in Machine Learning?

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In machine learning, overfitting is a very commonly used term. But what is overconfidence? Let’s talk about this today with some solid background and examples.

Well, if we think about overconfidence in our general life – what pops in our mind about it? It’s being excessively confident about something. Like we are driving a car to a destination where we went a long time ago. This time we are using a path from our memory and we are very confident that THIS is the right path. But eventually, we lost the path. This is being overconfident.

Okay okay! We, the humans may become overconfident about something. But what is overconfidence in a machine? Well, in machine learning, till now no model is perfect. Sometimes an algorithm fails to do what it is meant to do. Like we have built a model for classifying cat and dog images. It may happen that a model will identify a dog as a cat. Overconfidence is a situation where the model will predict a non-cat image as a cat image with significantly higher confidence. So, Overconfidence is a state when a model predicts a wrong label with higher confidence.

That’s all for this post. I hope this post will help you to enrich your understanding of overconfidence in machine learning. I am a learner who is learning new things and trying to share with others. Let me know your thoughts on this post. You can get more machine learning-related posts here.

I'm a Computer Science and Engineering student who loves to learn and spreading what is learnt. Besides I like cooking and photography!

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