Overfitting and underfitting are important things to think about when choosing features for a model.
Overfitting: This happens when a model learns too much from the training data, including mistakes or random noise. It tries so hard to fit every data point perfectly that it struggles to work well with new, unseen data.
Think of it like trying to draw a curvy line that goes through every single dot on a graph. It looks great for the training data but fails to predict what will happen in the future.
Underfitting: On the other hand, underfitting occurs when a model is too simple. It misses important features and does not learn enough from the data. As a result, it performs poorly, even with the training data.
Finding the right balance in feature selection is really important. It helps create a stronger model that can make better predictions!
Overfitting and underfitting are important things to think about when choosing features for a model.
Overfitting: This happens when a model learns too much from the training data, including mistakes or random noise. It tries so hard to fit every data point perfectly that it struggles to work well with new, unseen data.
Think of it like trying to draw a curvy line that goes through every single dot on a graph. It looks great for the training data but fails to predict what will happen in the future.
Underfitting: On the other hand, underfitting occurs when a model is too simple. It misses important features and does not learn enough from the data. As a result, it performs poorly, even with the training data.
Finding the right balance in feature selection is really important. It helps create a stronger model that can make better predictions!