Introduction
Feature engineering is the process of creating new features or transforming existing features to improve the performance of a machine learning model. Feature engineering is a critical step in the machine learning pipeline, as the quality and relevance of the features used can have a significant impact on the model's accuracy and performance.
There are many techniques used in feature engineering, including,
Feature extraction
This involves transforming raw data into a set of features that are meaningful and relevant to the problem being solved. For example, converting text data into numerical data through techniques such as bag-of-words or TF-IDF.
Feature scaling
This involves transforming features to a common scale, such as normalizing all features to have a mean of zero and a standard deviation of one. This can help ensure that features with large values do not dominate the model's decision making.
Feature selection
This involves selecting a subset of the features to use in the model, based on their relevance and importance for the problem being solved. Feature selection can help reduce overfitting and improve model interpretability.
Feature creation
This involves creating new features based on existing features, such as combining features, taking the logarithm of features, or transforming features into polynomials.
Dimensionality reduction
This involves reducing the number of features used in the model, by combining or transforming features into a lower-dimensional representation. This can help reduce overfitting, improve model interpretability, and reduce the computational cost of training a model.
Conclusion
Overall, feature engineering is an iterative process, where the engineer repeatedly tests different feature transformations and selects the best set of features for the model. The goal is to extract the most relevant information from the raw data, so that the model can learn to make accurate predictions.