Jump to content

Feature engineering: Difference between revisions

Feature engineering (view source)

Revision as of 13:12, 20 February 2023

1,753 bytes added ,  20 February 2023
no edit summary
No edit summary
No edit summary
Line 1: Line 1:
{{see also|Machine learning terms}}
==Introduction==
==Introduction==
[[Feature engineering]] is a critical process in [[machine learning]] that involves selecting, extracting, and transforming relevant [[feature]]s or variables from raw [[data]] to improve the accuracy and performance of [[machine learning models]]. Feature engineering is a complex and challenging process that requires domain knowledge, creativity, and expertise in data manipulation techniques. The objective of feature engineering is to transform raw data into a more suitable and informative representation that can be easily understood by machine learning models.
[[Feature engineering]] is a critical process in [[machine learning]] that involves selecting, extracting, and transforming relevant [[feature]]s or variables from raw [[data]] to improve the accuracy and performance of [[machine learning models]]. Feature engineering is a complex and challenging process that requires domain knowledge, creativity, and expertise in data manipulation techniques. The objective of feature engineering is to transform raw data into a more suitable and informative representation that can be easily understood by machine learning models.
Line 23: Line 24:


==How is feature engineering done in practice?==
==How is feature engineering done in practice?==
Feature engineering is a complex and iterative process that involves several steps, including data preprocessing, feature selection, feature extraction, and feature transformation. The first step is to clean and preprocess the raw data by removing missing values, outliers, and noise. The second step is to perform feature selection by identifying and selecting the most relevant features using various techniques. The third step is to perform feature extraction by creating new features from existing ones using mathematical or statistical transformations. Finally, the fourth step is to perform feature transformation by transforming the original features using various techniques.
#[[Data exploration]]: The initial step is to explore the data and gain an understanding of its features and their relationships with the target variable. Doing this helps identify any missing values, outliers, or other data quality issues that need to be addressed.
#[[Feature selection]]: The next step in feature selection is to identify features that are pertinent to the problem being solved. This process involves analyzing the correlations between features and the target variable, eliminating any redundant or low correlation elements that do not contribute to understanding the situation at hand.
#[[Feature transformation]]: Once features have been selected, they may need to be enhanced for use in a machine learning model. This can involve techniques like [[scaling]], [[normalization]], [[encoding categorical variables]] and [[feature cross|creating new features from existing ones]].
#[[Feature extraction]]: Sometimes, raw input data may lack relevant features or cannot be easily identified. In such cases, feature extraction can be employed to create new ones from the raw data. This can be accomplished using techniques like [[principal component analysis]] (PCA) or [[clustering]].
#Iteration: Feature engineering is an iterative process, in which the performance of the model is evaluated after each step and feature selection and transformation decisions are refined according to those results. This cycle continues until desired levels of model performance are reached.


In practice, feature engineering is a highly iterative and creative process that requires a deep understanding of
==Explain Like I'm 5 (ELI5)==
Feature engineering is like equipping yourself with the right tools to solve a puzzle.
 
Imagine you have a puzzle with pieces of all different shapes and sizes. With the appropriate tools, like magnifying glasses or tweezers, it will be much easier to put the pieces back together. This is similar to feature engineering - we select the appropriate "tools" so the computer can better comprehend data.
 
Machine learning is the practice of teaching computers to recognize things, like pictures of animals. To do this, we give the computer some "clues" or features about the image such as "it has four legs" or "it has pointy ears." Feature engineering involves selecting the best clues for giving to a computer so that it can make an accurate prediction.
 
By choosing the right features, we can help the computer learn more quickly and accurately. It's like having the right tools to put a puzzle together faster and with fewer mistakes.
 
[[Category:Terms]] [[Category:Machine learning terms]]
Retrieved from "http:///wiki/Special:MobileDiff/1765"