{"id":59495,"date":"2024-04-16T01:38:14","date_gmt":"2024-04-16T01:38:14","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=59495"},"modified":"2024-04-16T01:38:14","modified_gmt":"2024-04-16T01:38:14","slug":"what-is-the-term-for-the-process-of-converting-categorical-data-into-numerical-form-for-machine-learning","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/what-is-the-term-for-the-process-of-converting-categorical-data-into-numerical-form-for-machine-learning\/","title":{"rendered":"What is the term for the process of converting categorical data into numerical form for machine learning?"},"content":{"rendered":"

[amp_mcq option1=”Data normalization” option2=”Text vectorization” option3=”Feature scaling” option4=”One-hot encoding” correct=”option4″]<\/p>\n

The correct answer is D. One-hot encoding.<\/p>\n

One-hot encoding is a technique used to convert categorical data into numerical form for machine learning. It works by creating a new feature for each unique category in the data. For example, if you have a dataset of customers with the following categories: gender (male, female), age (18-24, 25-34, 35-44, 45-54, 55-64, 65+), and location (California, New York, Texas, Florida, Illinois), you would create six new features: gender_male, gender_female, age_18_24, age_25_34, age_35_44, age_45_54, age_55_64, age_65+, location_California, location_New York, location_Texas, location_Florida, location_Illinois.<\/p>\n

One-hot encoding is a common technique used in machine learning because it allows algorithms to work with categorical data in a way that they can understand. It is also relatively easy to implement and can be done in most programming languages.<\/p>\n

Here is a brief explanation of each of the other options:<\/p>\n