Overfitting and underfitting are two big problems in supervised learning.
Overfitting happens when a model learns the training data too much. It picks up on tiny details and random errors, which makes it struggle with new data it hasn't seen before.
Underfitting is when a model is too simple. It can’t understand the important patterns in the data, so it doesn’t perform well on either the training data or new data.
To fix these problems, try these methods:
Regularization: This means using strategies like L1 (Lasso) and L2 (Ridge) to make the model less complex.
Cross-validation: This helps you see how well the model works with new data by testing it in different ways.
Resampling: This means getting more data or changing the data you already have, which can help make the model stronger.
Finding the right balance in how complex your model is really matters for it to work well!
Overfitting and underfitting are two big problems in supervised learning.
Overfitting happens when a model learns the training data too much. It picks up on tiny details and random errors, which makes it struggle with new data it hasn't seen before.
Underfitting is when a model is too simple. It can’t understand the important patterns in the data, so it doesn’t perform well on either the training data or new data.
To fix these problems, try these methods:
Regularization: This means using strategies like L1 (Lasso) and L2 (Ridge) to make the model less complex.
Cross-validation: This helps you see how well the model works with new data by testing it in different ways.
Resampling: This means getting more data or changing the data you already have, which can help make the model stronger.
Finding the right balance in how complex your model is really matters for it to work well!