Feature Engineering

Definition of Feature Engineering

Feature Engineering: Feature engineering is the process of transforming raw data into a form that is more amenable to analysis or machine learning. This can involve things like aggregating data, transforming variables, or creating new features from existing variables. Feature engineering is an important part of data science, as it can help produce more accurate models and insights.

What is Feature Engineering used for?

Feature engineering is a process of transforming raw data into features that can be used for machine learning models. It is a key step in the development of predictive modeling systems and is often used to extract meaningful data from large datasets. Feature engineering involves selecting relevant features, transforming them into a suitable format, scaling them appropriately, and then incorporating them into the model. The main goal of feature engineering is to improve the accuracy and performance of machine learning models by improving the quality of input data. It involves the application of domain knowledge to identify new variables that can be used as predictors or to modify existing variables such that they become more suitable for prediction. This process can involve creating new features through combining existing variables, binning continuous variables, or introducing non-linear transformations. When done properly, it can significantly improve model performance by reducing over-fitting, reducing bias due to outliers or class imbalance, and improving generalizability when applied to unseen data points. Ultimately, feature engineering helps us better understand the underlying structure of our data and make better predictions with our machine learning models.