Feature engineering

See also: Machine learning terms

Introduction

Feature engineering is a critical process in machine learning that involves selecting, extracting, and transforming relevant features or variables from raw data to improve the accuracy and performance of machine learning models. Feature engineering is a complex and challenging process that requires domain knowledge, creativity, and expertise in data manipulation techniques. The objective of feature engineering is to transform raw data into a more suitable and informative representation that can be easily understood by machine learning models.

What are features in machine learning?

Features in machine learning refer to the attributes or characteristics of the data that can be used to describe or differentiate different classes or groups. Features are typically represented as columns in a dataset, where each row corresponds to an example or data point. For example, in a dataset containing information about houses, features may include the number of bedrooms, the size of the living room, the age of the house, and the location of the house.

Features are essential in machine learning, as they provide the basis for learning patterns and making predictions. However, not all features are equally important, and some may be irrelevant, redundant, or noisy, which can negatively impact the performance of machine learning models. Therefore, the process of feature engineering is crucial in identifying and selecting the most relevant and informative features for a particular problem.

Why is feature engineering important?

Feature engineering is important in machine learning for several reasons. First, it helps to improve the performance and accuracy of machine learning models by providing a more informative and discriminative representation of the data. Second, it helps to reduce the dimensionality of the data by removing irrelevant or redundant features, which can simplify the learning process and improve computational efficiency. Third, it can help to address issues such as overfitting and underfitting by providing a better balance between bias and variance. Finally, feature engineering can help to enhance the interpretability and explainability of machine learning models, which is essential in many real-world applications.

What are the types of feature engineering?

Feature engineering can be broadly classified into three main types: feature selection, feature extraction, and feature transformation.

Feature selection

Feature selection involves selecting a subset of relevant features from a larger set of features. This can be done using various techniques such as correlation analysis, mutual information, chi-square tests, and recursive feature elimination. The goal of feature selection is to reduce the dimensionality of the data while maintaining or improving the performance of the machine learning model.

Feature extraction

Feature extraction involves creating new features from existing features by applying various mathematical or statistical transformations. Examples of feature extraction techniques include Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Non-negative Matrix Factorization (NMF). The goal of feature extraction is to create a more informative and compact representation of the data that can improve the performance of machine learning models.

Feature transformation

Feature transformation involves transforming the original features by applying mathematical or statistical functions such as logarithmic, exponential, or power functions. The goal of feature transformation is to normalize the data or make it more suitable for a particular machine learning model. Examples of feature transformation techniques include scaling, centering, and normalization.

How is feature engineering done in practice?

1. Data exploration: The initial step is to explore the data and gain an understanding of its features and their relationships with the target variable. Doing this helps identify any missing values, outliers, or other data quality issues that need to be addressed.
2. Feature selection: The next step in feature selection is to identify features that are pertinent to the problem being solved. This process involves analyzing the correlations between features and the target variable, eliminating any redundant or low correlation elements that do not contribute to understanding the situation at hand.
3. Feature transformation: Once features have been selected, they may need to be enhanced for use in a machine learning model. This can involve techniques like scaling, normalization, encoding categorical variables and creating new features from existing ones.
4. Feature extraction: Sometimes, raw input data may lack relevant features or cannot be easily identified. In such cases, feature extraction can be employed to create new ones from the raw data. This can be accomplished using techniques like principal component analysis (PCA) or clustering.
5. Iteration: Feature engineering is an iterative process, in which the performance of the model is evaluated after each step and feature selection and transformation decisions are refined according to those results. This cycle continues until desired levels of model performance are reached.

Explain Like I'm 5 (ELI5)

Feature engineering is like equipping yourself with the right tools to solve a puzzle.

Imagine you have a puzzle with pieces of all different shapes and sizes. With the appropriate tools, like magnifying glasses or tweezers, it will be much easier to put the pieces back together. This is similar to feature engineering - we select the appropriate "tools" so the computer can better comprehend data.

Machine learning is the practice of teaching computers to recognize things, like pictures of animals. To do this, we give the computer some "clues" or features about the image such as "it has four legs" or "it has pointy ears." Feature engineering involves selecting the best clues for giving to a computer so that it can make an accurate prediction.

By choosing the right features, we can help the computer learn more quickly and accurately. It's like having the right tools to put a puzzle together faster and with fewer mistakes.