The correct answer is: D. the training and testing examples are sampled from different distributions.
Overfitting occurs when a model learns the training data too well and is unable to generalize to new data. This can happen when the model is too complex or when the training data is not representative of the data that the model will be used on.
Underfitting occurs when a model does not learn the training data well enough and is unable to make accurate predictions on new data. This can happen when the model is too simple or when the training data is not large enough.
Regularization is a technique that can be used to prevent overfitting. It does this by adding a penalty to the model’s loss function that discourages the model from becoming too complex.
When the training and testing examples are sampled from different distributions, it can be difficult for the model to generalize to the testing data. This is because the model may have learned to fit the noise in the training data, rather than the underlying patterns.
In the given scenario, the model is giving very high accuracy on the training data, but much lower accuracy on the validation data. This is a classic sign of overfitting. The training data and validation data are likely sampled from the same distribution, so option D is false.