Question

If you can fit the training data, but find large errors on the testing data, then you probably have _________

a) good fitting
b) overfitting
c) underfitting
d) all of the above

Answer 1

If you can fit the training data, but find large errors on the testing data, then you probably have overfitting. Overfitting occurs when a model is too complex and fits the training data too closely, causing it to perform poorly on unseen data.

Step-by-step explanation:

If you can fit the training data, but find large errors on the testing data, then you probably have overfitting. Overfitting occurs when a model is too complex and fits the training data too closely, causing it to perform poorly on unseen data.

For example, imagine you are trying to predict housing prices based on variables such as number of bedrooms, square footage, and location. If you overfit the model by including too many variables or using a high-degree polynomial regression, the model may fit the training data well but fail to accurately predict housing prices for new data points.

Underfitting, on the other hand, occurs when a model is too simple and fails to capture the patterns in the training data. Good fitting refers to the ideal balance between capturing the patterns in the data without overfitting or underfitting.