4. What could be the cause of a model yielding high training accuracy and low out-of-sample accuracy? The model is training on a small training set ,so it is underfitting. When we perform multiple train/test splits using the same dataset, it will cause overfitting. The model is training on a small training set, so it is overfitting. The model is training on the entire dataset, so it is underfitting.

The correct answer is: The model is training on a small training set, so it is overfitting.<br /><br />Explanation: When a model yields high training accuracy but low out-of-sample accuracy, it is likely that the model is overfitting to the training data. Overfitting occurs when a model is too complex and learns the noise and details in the training data, including the errors, rather than the underlying distribution. This results in the model performing well on the training data but poorly on new, unseen data (out-of-sample data). In this case, the model is not generalizing well to new data, which is why the out-of-sample accuracy is low.