The correct answer is C. 1 and 3.
Overfitting is a phenomenon that occurs when a machine learning model learns the training data too well and is unable to generalize to new data. This can happen when the model has too many parameters, or when the training data is not representative of the data that the model will be used on.
The number of observations in a dataset has a direct impact on the risk of overfitting. With fewer observations, the model will have less data to learn from and will be more likely to overfit. With more observations, the model will have more data to learn from and will be less likely to overfit.
However, it is important to note that the number of observations is not the only factor that affects overfitting. The complexity of the model, the distribution of the data, and the choice of hyperparameters can also play a role.
In general, it is advisable to use as many observations as possible when training a machine learning model. This will help to reduce the risk of overfitting and improve the model’s performance on new data.
Here is a more detailed explanation of each option:
- In case of fewer observations, it is easy to overfit the data.
This is because the model will have less data to learn from and will be more likely to learn the training data too well. This can lead to the model being unable to generalize to new data.
- In case of fewer observations, it is hard to overfit the data.
This is not true. As explained above, fewer observations make it more likely for the model to overfit.
- In case of more observations, it is easy to overfit the data.
This is true, as the model will have more data to learn from and will be more likely to learn the training data too well. This can lead to the model being unable to generalize to new data.
- In case of more observations, it is hard to overfit the data.
This is not true. As explained above, more observations make it more likely for the model to overfit.