This content originally appeared on DEV Community and was authored by Muthoni, Rogers
Introduction
Feature engineering refers to the selection, manipulation, and transformation of data (mainly raw) into features that can be used for effective data analysis or the development of machine learning models. Data scientists use feature engineering to create new features that can more precisely represent the problem at hand. This is achieved using a set of techniques that aid in highlighting the most important patterns, trends, or relationships, making it easier for models to learn from the data effectively.
- In this article, we will look into;
- What is feature engineering?
- Feature engineering process.
- The importance of feature engineering.
- Feature engineering techniques.
- Tools for feature engineering.
What is Feature Engineering?
When working with Machine Learning models, feature engineering is essential in building reliable machine learning pipelines. It involves selecting, manipulating, and transforming raw data into features that can be used with machine learning models. The new features (or variables) are usually not included in the original dataset. The main aim of feature engineering is to make transformations on datasets to increase the efficiency or accuracy of the models. Feature engineering is considered one of the most crucial tasks that significantly determines the outcome of models. To ensure that a certain machine learning algorithm performs optimally, the features of the input data should be engineered effectively.
Feature Engineering Process
Several processes are involved in feature engineering. These are discussed below;
- Feature creation: This involves creating new features that will be helpful during model development. Typically, this may involve removing or adding features in the dataset.
- Transformation: This refers to the function of transforming features in the dataset from one representation to another.
- Feature extraction: This involves the extraction of features from the dataset without necessarily distorting significant information or relationships existing in the original data.
- Exploratory data analysis: Exploratory Data Analysis (EDA) is the approach used to understand the general patterns, trends, or relationships existing in the data. Normally, EDA involves the use of graphs, charts, or summary statistics to perform initial investigations mainly to identify patterns and trends, and sometimes to spot anomalies in the data.
- The Importance of Feature Engineering
Feature engineering is crucial in any machine learning project. As mentioned, features produced during feature engineering are used by machine learning algorithms to improve performance and accuracy, in other words, to improve the results of the models. Since data scientists spend most of their time with data, making models accurate and reliable is therefore essential.
When feature engineering is done correctly
Feature Engineering Techniques
Tools for Feature Engineering
This content originally appeared on DEV Community and was authored by Muthoni, Rogers
Muthoni, Rogers | Sciencx (2024-08-20T15:29:29+00:00) Understanding Feature Engineering. Retrieved from https://www.scien.cx/2024/08/20/understanding-feature-engineering/
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