The correct answer is A. Data Encoding.
Data encoding is the process of converting data from one format to another. In the context of machine learning, data encoding is used to convert text data into a numerical format so that it can be used in machine learning algorithms.
There are a number of different data encoding techniques, but the most common is one-hot encoding. One-hot encoding involves creating a new feature for each unique value in the text data. For example, if the text data contains the words “cat”, “dog”, and “horse”, then one-hot encoding would create three new features, one for each word.
Data encoding is an important step in the machine learning process. It allows machine learning algorithms to work with text data, which is a common type of data.
Here is a brief explanation of each option:
- A. Data Encoding: Data encoding is the process of converting data from one format to another. In the context of machine learning, data encoding is used to convert text data into a numerical format so that it can be used in machine learning algorithms.
- B. Data Tokenization: Data tokenization is the process of breaking text data into smaller pieces, called tokens. Tokens can be individual words, phrases, or even characters.
- C. Data Parsing: Data parsing is the process of converting data from one format to another while also making sure that the data is valid. For example, data parsing can be used to convert a date from a string format to a date-time format.
- D. Data Transformation: Data transformation is the process of changing the format or structure of data. Data transformation can be used to clean data, normalize data, or aggregate data.