While exploring a dataset for training a two$-$class $($binary$)$ classification problem, you notice the following properties of the data set:

Certain features exhibit noticeable separation in the values or categories based on the categories of the label.

Certain features exhibit little separation in the values or categories based on the categories of the label.

Which two of the following feature selection actions should you take before training the model?

Group of answer choices

The features exhibiting separation should be retained in the dataset.

All features should be retained in the data set.

Features should be randomly selected to avoid biased in training the model.

The features with poor separation should be pruned from the dataset.