Machine Learning is considered to be powered by Data; however, when using raw data, creating models is usually inaccurate and unreliable until that data has been transformed into a format that is accepted and usable by our machine learning algorithms, resulting in unlocking the true value of the data we have. This transformation of the data is referred to as Feature Engineering.
Feature Engineering is one of the most significant components in determining whether the model created through Machine Learning achieves an acceptable level of success. In many, if not most, projects in the real world that we work on, it is not the algorithm that yields the difference in producing an average model compared to a highly accurate model, but rather, it is the degree to which Feature Engineering was accomplished. This blog will offer an overview, address the significance of Feature Engineering, review the various types of Feature Engineering, and describe the role Feature Engineering plays in achieving the overall success of Machine Learning.
What Is Feature Engineering?
Feature engineering is the creation, refinement, and selection (or transformation) of physical and extracted statistical features from unstructured (raw) data to make machine learning models more accurate.
To put it simply, “features” are the variables that input your data into the machine learning model, thereby allowing the model to generate a prediction. For instance:
- For predicting house prices, possible “features” might include square footage, number of bedrooms, geographical location, age of the house, etc.
- To detect fraud, the “features” could be transaction amount, transaction frequency, device information, user behavior, etc.
Unstructured data is often disorganized, missing information, and/or unusable. Feature engineering takes that unstructured data and turns it into meaningful signals so that algorithms can detect patterns with greater accuracy.
Why Is Feature Engineering Important?
Feature engineering is crucial for machine learning because the algorithms lack an inherent comprehension of the actual meaning of the data. The algorithms depend completely upon the numerical and categorical input they receive.
Here are five main implications of feature engineering being so important:
- Increased Model Prediction Accuracy
A good set of features will make the data show you important patterns and relationships. More predictive power comes from better features, which means that even if you are using a relatively simple algorithm, if the features presented to the model are good features, the model can perform much better than a much more difficult algorithm that does not offer good features.
- Noise Reduction
Raw data sets typically contain too many irrelevant and/or repeated variables. Feature engineering will help reduce the number of unnecessary variables in a raw data set and concentrate on the relevant signals within the raw data.
- Increased Interpretability
Thoughtfully built features will also allow the model that is built on those features to be more interpretable. For example, building a feature for “customer lifetime value” would provide more clarity on the business meaning of that feature than merely looking at the raw transaction logs of that customer.
- Realizing Robustness of Real World Data
Most real-life data is often populated with missing values, outliers, time dependence, and non-linear relationships. Feature engineering will help manage some of these challenges with the data set.
- Improved Generalization
Well-engineered features will lead to generalization of the resulting models and less overfitting, which can lead to better performance when those models are applied to real-world data.
Key Components of Feature Engineering
There are several components to feature engineering, many of which correlate with one another. This will depend on the dataset and the nature (type) of the problem.
Data Cleaning
Before any features can be created, the underlying data needs to be cleaned. This includes:
- Dealing with missing values
- Removing duplicates
- Correcting inconsistent entries
- Managing outliers
The cleanliness of the data ensures that the resultant features will be reliable and significant.
Feature Transformation
Certain raw features may need to be transformed to become useful features. Common feature transformations may include:
- Log transformations for values that are highly skewed
- Scaling (either normalization or standardization)
- Encoding categorical variables (e.g., converting “red” to [1,0,0], “green” to [0,1,0], etc.)
- Splitting date and time data into usable components (day, month, year, day of the week)
For example, instead of using a raw timestamp, a “day of week” may phase into a regular cycle.
Feature Creation
Feature creation refers to the creation of new features based on the original features. Examples include:
- Combining features into new variables (e.g., total purchase = quantity x price)
- Creating Interaction Terms
- Creating aggregate values (e.g., average monthly spending)
- Creating ratio/percentage values.
New features may often be used to ultimately find hidden relationships in the data.
Feature Selection
Not all of the obtained characteristics will have equal contributions to modelled predictions. Some may be irrelevant to the prediction model or redundant. Feature selection techniques will assist in:
- Maximizing model performance by selecting the most significantly informative characteristics.
- Minimizing computational cost by limiting the number of variables used in the model (ex. feature selection may limit the number of predictors).
- Preventing overfitting due to too many contributing variables.
Methods of Conducting Feature Selection include correlation analysis, statistical evaluation of features, and measures of feature importance via the use of machine learning models.
Feature Extraction
Feature extraction involves the reduction of datasets while preserving essential information. Common methods used in feature extraction include:
- Principal Component Analysis (PCA)
- Autoencoders
- Text Vectorization, such as TF-IDF
Types of Feature Engineering Techniques
Feature engineering approaches depend on the form of data being used to create features.
Numerical Data
Commonly used methods to create numerical features include:
- Converting (binning) a continuous variable into classes,
- Creating polynomial features to similarly capture non-linear relationships,
- Scaling and normalising numerical variables,
- Capping numerical outliers.
Categorical Data
To input categorical variables into a machine learning model, they must first be encoded in a numerical format. This can be done through several methods:
- One-hot encoding
- Label encoding
- Target encoding
- Frequency encoding
The encoding method you select can impact the model’s performance.
Text Data
Text data must be pre-processed using several different methods:
- Tokenisation
- Removing stop words
- Stemming and Lemmatization
- Representing text as a vector using TF-IDF
- Building word embeddings via a vector representation.
These methods convert the text input into a numerical format that can then be used in a model.
Time-series Data
Features must be created based on the time when working with time-series data:
- Lag variables,
- Rolling averages,
- Time-based indicators (hour of day, day of week, month),
- Trend and seasonal components.
When developing features for a time-series dataset, the time-series data must be modelling-ready prior to any predictive model being created.
Real-World Example of Feature Engineering
For a telecom company to create a prediction system for customer churning, we would collect raw data about customers, including:
- Customer ID
- Call records
- Monthly billings
- Service usage logs
- Complaint history
After collecting these data points, we can generate engineered features (variables) from the raw data, including the following:
- Average monthly billing for the last 6 months
- Number of complaints within the last 90 days
- Change in data usage compared to the previous month
- Remaining contract length
- Frequency of delayed bill payments
Engineered features typically tend to have greater predictive ability than raw logs or records.
In a lot of business environments, the degree of success of churn prediction systems is based primarily on the quality of the feature engineering rather than the algorithm(s) utilised, such as logistic regression or random forests.
Impact of Feature Engineering on Different Algorithms
Some algorithms respond differently to feature sets:
- Linear models are optimized with well-scaled/transformed features.
- Tree-based models pass through non-linear relationships much better and need less scalability.
- Neural networks are best optimized with normalized inputs and large amounts of informative data.
As deep learning has become more advanced, feature engineering is still an important step when working with structured data.
Automation in Feature Engineering
Tools that assist with Feature Engineering have increased in number due to the emergence of Automated Machine Learning (AutoML). These tools create and evaluate features automatically.
However, domain knowledge is very important in Feature Engineering (e.g., domain experts can often appreciate business-specific insights that automated systems might miss).
Some examples include:
Cardiologists have domain expertise that allows them to understand the creditworthiness of their patients, which helps them create valuable indicators of risk in a given financial situation.
General Practitioners (GPs) have domain knowledge that enables them to determine which clinically relevant features should be included in the Feature Engineering process.
The best results are obtained through a combination of human insight and automation.
Challenges in Feature Engineering
Feature engineering is extremely beneficial, but it also has difficulties.
- Time-consuming
Feature engineering often takes much time, failure, and effort, as well as requires many inputs of Domain Knowledge
- Data Leakage
When features are created incorrectly, they could potentially provide the model with access to future information in the training set. This could then create a false measure of success for that model.
- Overfitting
Making several complicated features super can lead to models memorizing patterns instead of determining them.
- Scaleability Issues
Large datasets often require the development of a very efficient pipeline to perform the transformational processes required for creating the features of those large datasets, to eventually do the feature engineering.
To deal with these challenges, planning, validating, and testing appropriately is necessary.
The Future of Feature Engineering
Feature engineering will continue to evolve as machine learning continues to develop and will also include the following trends in the future.
- Automated tools for generating features.
- Storage of features in a central repository for easy management.
- Real-time digital feature pipelines.
- Co-development of AI domains within existing industries.
While automation provides for efficiency and cost savings, human creativity and domain knowledge will still be the most important aspects in developing high-quality features.
Conclusion
The process of creating useful inputs for machine-learning algorithms is called feature engineering. This process involves converting data from its initial form into a representation that enables algorithms to learn from the data.
Even with advances in technology and the average algorithm’s ability to predict accurately, creating well-engineered features continues to produce the most substantial results in predicting future events.
By creating, cleaning, transforming, selecting, and engineering features from data, data scientists can create much more accurate, interpretable, and generalisable models. When machine Learning continues to be expanded upon in industries such as finance, healthcare, retail, and manufacturing, applying best feature engineering practices will result in more intelligent and reliable artificial intelligence systems.
Ultimately, machine learning isn’t about the ability of algorithms to make predictions by choosing the “best” algorithm; rather, it is about providing the algorithm with the “right’ data in a format that can be used for making appropriate predictions. Feature engineering is how data scientists accomplish this.