{"id":171637,"date":"2023-10-05T21:16:47","date_gmt":"2023-10-05T20:16:47","guid":{"rendered":"https:\/\/liora.io\/en\/?p=171637"},"modified":"2026-02-17T14:58:29","modified_gmt":"2026-02-17T13:58:29","slug":"feature-engineering-importance-for-machine-learning","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/feature-engineering-importance-for-machine-learning","title":{"rendered":"Feature Engineering: Importance for Machine Learning"},"content":{"rendered":"\n

Feature Engineering involves extracting features from raw data to solve specific domain-specific problems using machine learning. Discover everything you need to know: definition, algorithms, use cases, training courses…<\/strong><\/p>\n\n\n\n

Artificial intelligence<\/a> is increasingly used in all fields. However, to fully unleash its potential, a predictive analysis model<\/a> requires leveraging the available data. To achieve this, it’s essential to choose the right algorithm<\/a> and train machine learning models.<\/a> In reality, the most crucial aspect is utilizing “Feature Engineering.”<\/strong><\/p>\n\n\n\n

Indeed, the features of the data have a direct impact on predictive models and their results. The more carefully prepared and chosen the features are, the more accurate the results will be. They should describe the inherent structure within the data. In general, results depend on the chosen model, available data, and prepared features. Problem framing and the metrics<\/strong> used to estimate accuracy also play a significant role.<\/strong><\/p>\n\n\n\n

Even if a model isn’t optimal, it can still yield good results. The key is to use good features, which allows for the use of less complex, faster-to-run models that are simpler to understand and maintain. Likewise, good feature engineering<\/strong> can yield good results even if the chosen parameters aren’t optimal. So, there’s no need to endlessly search for the best model and the most optimized parameters, as long as you have the right features.<\/p>\n\n\n\n

These features allow you to get closer to the underlying problem and represent the data accurately. So, what is Feature Engineering?<\/p>\n\n\n\n

What is Feature Engineering ?<\/h2>\n\n\n\n

Feature Engineering<\/strong> is a process that involves transforming raw data into features that more precisely represent the underlying problem for a predictive model. Simply put, it’s about applying domain knowledge to extract analytical representations from raw data and preparing them for machine learning. This is the first step in developing a predictive machine learning model.<\/strong> It helps increase the model’s accuracy on new, unseen data.<\/p>\n\n\n\n

It’s important to remember that machine learning algorithms<\/strong> learn a solution to a problem from sample data. Thus, Feature Engineering determines the best representation of the sample data for learning<\/strong> the solution to the problem. This is highly significant because the success of an artificial intelligence or machine learning project<\/a> often depends on the data representation. The algorithms must be able to understand the inputs. Feature Engineering relies on a set of well-defined procedures and methods. The procedures to use vary depending on the data, and it’s through experience and practice that one learns which ones to use in a given context.<\/p>\n\n\n\n

\n