The correct answer is: FALSE.
Overfitting is a common problem in machine learning, and it occurs when a model learns the training data too well and is unable to generalize to new data. This can happen when a model is trained on a small amount of data, or when the data is not representative of the real world.
However, overfitting is not more likely when you have a huge amount of data to train. In fact, having more data can actually help to prevent overfitting. This is because more data provides the model with more information to learn from, which can help it to generalize better.
There are a number of things that you can do to prevent overfitting, such as using regularization techniques, cross-validation, and feature selection. You can also try to collect more data, if possible.
Here is a brief explanation of each option:
- A. TRUE. This is incorrect because overfitting is not more likely when you have a huge amount of data to train.
- B. FALSE. This is the correct answer.
- C. UNKNOWN. This is incorrect because it is impossible to say for sure whether overfitting is more likely when you have a huge amount of data to train without knowing more about the specific situation.
- D. NOT ENOUGH INFORMATION. This is incorrect because it is impossible to say for sure whether overfitting is more likely when you have a huge amount of data to train without knowing more about the specific situation.