In supervised learning, two important ideas are overfitting and underfitting. These affect how well a model works.
Overfitting happens when a model learns the training data too well. It picks up on all the little details and noise, treating them as if they are real patterns. This makes the model very accurate on the training data but not good at predicting new, unseen data. The model struggles to tell the difference between important information and unimportant noise. Mathematically, we say that the model becomes too complex compared to the amount of training data it has. This leads to high variance (sensitivity to changes in data) and low bias (not making many assumptions).
Underfitting, on the other hand, occurs when a model is too simple. It can't see the real trends in the data. For example, if we use a linear model to analyze complex, wavy data, it won’t perform well. The model ends up being bad at both the training data and new data because it has high bias (making too many assumptions) and low variance (not being sensitive enough to changes).
To fix these problems, we need to build strong models. One way to check for overfitting is through cross-validation. This technique tests the model on different sets of data to see how well it performs.
Another method is regularization. This adds a penalty when the model uses very large numbers to avoid being too complex. It helps to keep the model simpler.
For underfitting, we can make the model more complex or create new features from the data so it can better find the necessary patterns.
At the end of the day, finding the right balance between overfitting and underfitting is essential to make effective supervised learning models.
In supervised learning, two important ideas are overfitting and underfitting. These affect how well a model works.
Overfitting happens when a model learns the training data too well. It picks up on all the little details and noise, treating them as if they are real patterns. This makes the model very accurate on the training data but not good at predicting new, unseen data. The model struggles to tell the difference between important information and unimportant noise. Mathematically, we say that the model becomes too complex compared to the amount of training data it has. This leads to high variance (sensitivity to changes in data) and low bias (not making many assumptions).
Underfitting, on the other hand, occurs when a model is too simple. It can't see the real trends in the data. For example, if we use a linear model to analyze complex, wavy data, it won’t perform well. The model ends up being bad at both the training data and new data because it has high bias (making too many assumptions) and low variance (not being sensitive enough to changes).
To fix these problems, we need to build strong models. One way to check for overfitting is through cross-validation. This technique tests the model on different sets of data to see how well it performs.
Another method is regularization. This adds a penalty when the model uses very large numbers to avoid being too complex. It helps to keep the model simpler.
For underfitting, we can make the model more complex or create new features from the data so it can better find the necessary patterns.
At the end of the day, finding the right balance between overfitting and underfitting is essential to make effective supervised learning models.