In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least . . . . . . . . valid options

The correct answer is C. 3.

Scikit-learn offers at least 3 valid options for encoding categorical labels:

• One-hot encoding: This is the most common approach. It creates a new feature for each unique category in the target dataset. For example, if the target dataset has the categories “red”, “green”, and “blue”, then one-hot encoding would create 3 new features, one for each category.
• Label encoding: This is a simpler approach that maps each category to a unique integer. For example, if the target dataset has the categories “red”, “green”, and “blue”, then label encoding would map “red” to 0, “green” to 1, and “blue” to 2.
• Ordinal encoding: This is a more complex approach that takes into account the order of the categories. For example, if the target dataset has the categories “red”, “green”, and “blue”, then ordinal encoding would map “red” to 1, “green” to 2, and “blue” to 3.

The choice of encoding method depends on the specific problem and the algorithm being used. For example, some algorithms work better with one-hot encoding, while others work better with label encoding. It is important to experiment with different encoding methods to see which one works best for your particular problem.