When we talk about supervised learning, choosing the right features is really important. If we pick the wrong ones, it can mess up how accurate our model is. Here are some reasons why this happens:
Unhelpful Features: If we include features that don’t give us useful information, it can cause problems. The model may get confused and try to learn from noise instead of the real patterns.
Too Many Features: Using too many features can make the model too complicated. This can lead to something called the "curse of dimensionality." When this happens, our predictions become less reliable.
Related Features: If some features are related to each other, it can create redundancy. This makes it hard to figure out which features are really important.
In short, picking smart features helps keep our models simple and improves how well they perform!
When we talk about supervised learning, choosing the right features is really important. If we pick the wrong ones, it can mess up how accurate our model is. Here are some reasons why this happens:
Unhelpful Features: If we include features that don’t give us useful information, it can cause problems. The model may get confused and try to learn from noise instead of the real patterns.
Too Many Features: Using too many features can make the model too complicated. This can lead to something called the "curse of dimensionality." When this happens, our predictions become less reliable.
Related Features: If some features are related to each other, it can create redundancy. This makes it hard to figure out which features are really important.
In short, picking smart features helps keep our models simple and improves how well they perform!