Overfitting and underfitting are common problems that come up when training models in machine learning. Let’s break them down:

1. Overfitting

• This happens when the model learns too much from the training data, including the distractions or "noise."
• Here’s how it looks: The model might do really well on the training data, showing high accuracy (like 98%). But when we test it on new, unseen data, its accuracy drops way down—sometimes below 70%.
• A good example of this is when we use complex models with a lot of details, which can cause them to remember every little thing rather than finding the big patterns.

2. Underfitting

• Underfitting is the opposite issue. This occurs when the model is too simple to recognize important patterns in the data.
• With underfitting, the model won’t do well on either the training data or new data, often scoring below 60% accuracy.
• A common example is using a straight line (linear model) to try and fit data that is actually curved or has a complex shape.

To avoid these issues, it’s important to find the right balance in how complex our model should be. This means making sure our model is just right—not too complicated and not too simple.