What is Feature Engineering ?

Опубликовано: 17 Январь 2025
на канале: Machine Learning

#datascience #engineering #technology

Feature engineering is the process of using the knowledge to choose variables wisely for extracting features from raw data. The motivation is to use these extra features to improve the quality of results from a machine learning process, compared with supplying only the raw data to the machine learning process. A correlation analysis is helpful when doing feature engineering.

Ques - What is Feature Engineering ?
Ans - Feature engineering is a machine learning technique that leverages data to create new variables that aren't in the training set.

Ques - Why feature engineering is important in machine learning?
Ans - Feature engineering in machine learning is more than selecting the appropriate features and transforming them. Not only does feature engineering prepare the data set to be compatible with the algorithm, but it also improves the performance of the machine learning models.

Ques - What are the different types of features in machine learning?
Ans - There are three distinct types of features: quantitative, ordinal, and categorical.These feature types can be ordered in terms of how much information they convey. Quantitative features have the highest information capacity followed by ordinal, and categorical. Patterns are Range, variance, standard deviation for Quantitative; Quantiles for Ordinal and Boolean for Catagorical.