The correct answer is C. Feature Engineering.
Feature engineering is the process of using domain knowledge to extract new features from raw data. These features are then used as input to machine learning algorithms.
Data sampling is the process of selecting a subset of data from a larger dataset. This is done to reduce the size of the dataset, which can improve the performance of machine learning algorithms.
Data wrangling is the process of cleaning and organizing data. This is done to make the data more consistent and easier to work with.
Data reduction is the process of reducing the dimensionality of data. This is done to improve the performance of machine learning algorithms.
Here are some examples of feature engineering:
- In a spam filtering system, one feature might be the number of exclamation marks in an email.
- In a fraud detection system, one feature might be the number of transactions made in a short period of time.
- In a medical diagnosis system, one feature might be the patient’s age.
Feature engineering is a critical step in machine learning. It can make the difference between a successful and unsuccessful model.