Description: Feature Engineering Techniques are a set of methods used in the field of Machine Learning to create new features or modify existing ones in order to improve the performance of predictive models. These techniques are fundamental, as the quality and relevance of the features used in a model can significantly influence its ability to generalize and make accurate predictions. Feature engineering involves a creative and analytical process, where the goal is to transform raw data into more useful representations. This can include data normalization, the creation of derived variables, encoding categorical variables, and the removal of irrelevant or redundant features. Additionally, techniques such as feature selection can be applied to identify the most important variables for the model, and feature extraction aims to reduce the dimensionality of the data. In a Big Data environment, where data volumes are enormous and complex, feature engineering becomes even more crucial, as it allows analysts and data scientists to effectively manage and extract value from large datasets.
History: Feature engineering has evolved over the past few decades, especially with the rise of Machine Learning and the availability of large volumes of data. Although the concept of data manipulation to improve models is not new, its formalization as a discipline within machine learning began to take shape in the 1990s, when more complex algorithms were developed that required a more systematic approach to data preparation. With the growth of Big Data in the 2000s, feature engineering became an essential practice for the success of Machine Learning models, as unstructured and semi-structured data began to be the norm.
Uses: Feature engineering techniques are used in various Machine Learning applications, including sales forecasting, sentiment analysis, fraud detection, and customer segmentation. In the healthcare field, they are applied to predict diseases from clinical data. In the financial sector, they are used to assess credit risks and detect suspicious transactions. Additionally, in image processing and speech recognition, feature engineering is critical for extracting relevant information from the data.
Examples: A practical example of feature engineering is creating variables from time data, such as extracting the day of the week or the month from a date to improve sales prediction. Another case is the encoding of categorical variables, where categories are transformed into numerical values using techniques like One-Hot Encoding. In the field of fraud detection, features can be created that represent unusual behavior patterns from historical transactions.
