Labeling is really important for making supervised learning work well. Let’s break down why it matters:

1. Helping the Model Learn: Supervised learning is like teaching the model using labeled information. Each label is like a sign pointing the model in the right direction for what it should expect. Without labels, it’s a bit like giving a student a test without any lessons first!

2. Dividing the Data: Well-labeled data is key for splitting your information into three main parts:

   • Training Set: This is where the model learns, so having the right labels is super important here.
   • Validation Set: This part helps improve the model’s settings. If the labels are wrong, the model can make bad choices.
   • Test Set: This is the final check to see how well the model performs. If there are mistakes in the labels, you might think the model is doing better or worse than it actually is.

3. Effect on Accuracy: Research shows that if labels aren’t accurate, the model's accuracy can drop a lot. The model ends up learning from mistakes, which affects how it makes predictions. It’s like trying to find your way using a torn map—you’re likely to get lost!

In short, having good labels is super important for any successful supervised learning model. Make sure to take the time to get it right!