5 Reasons Why Feature Engineering is Challenging
Feature engineering is an important part of leveraging big datasets. Even with the right technical skills and domain knowledge, it can still be a time consuming process. This blog article will go over feature engineering, the five biggest challenges associated with it, and how automated feature engineering can help.
What is feature engineering?
Turning data into insights requires more than knowing the right questions to ask and being able to translate them into algorithms. It also requires identifying and creating the most accurate predictive indicators – features – in the dataset. Data by itself doesn’t give you the insights you need. Combining the data to find possible predictors to your questions is how you extract maximum value from your datasets. The foundation of feature engineering includes ideating, selecting, and creating useful features in your datasets, helping identify connections, correlations, and patterns to explore using a machine learning model.
The main goal of feature engineering is to make predictive models more accurate – it helps derive the most valuable insights from big data sets. The process of feature engineering involves using domain knowledge to select and transform relevant variables from raw data when building a predictive model. It also helps to build more complex models than could be done with only raw data. Engineering new, relevant features that improve predictive model performance is one of the hardest problems in data science. The variety of data has grown exponentially and there is almost an infinite amount of opportunities for relevant and impactful signals and sources.
According to a Forbes survey, data scientists spend 80% of their time on data preparation. This metric is impressive because it shows the importance of feature engineering in data science. Real-world data is almost always messy. Before deploying a machine learning algorithm, the raw data that it will be trained on must first be transformed into a consumable format. This is called data preprocessing, and feature engineering is a component of this process.
The feature engineering process involves:
- Feature construction: Constructing new features from the raw data. Feature construction requires comprehensive knowledge of the data and the underlying problem to be solved with the data.
- Feature selection: Selecting a subset of available features that are most relevant to the problem for model training.
- Feature extraction: Creating new and more useful features by combining and reducing the number of existing features. Principal component analysis (PCA) and embedding are some methods used for feature extraction.
Why is it so challenging?
1) It requires an advanced technical skillset.
Feature engineering requires deep technical skills, detailed knowledge of data engineering, and the way ML algorithms work. It demands a specific skillset including programming and understanding how to work with databases. Most feature engineering techniques require Python coding skills. Testing the impact of newly created features involves repetitive trial and error work. Sometimes the insights gained do not lead to results the practitioner wants or expects, such as worse model accuracy, instead of improvements, after adding new features. To be able to “teach” an algorithm the relevant information that humans know, and to even understand what is known on the human side, is another nuanced skill. Having experience working with databases is also essential, and working with big data might have the extra hurdle of using big data systems like Hadoop and Cassandra. Knowing and understanding the quality of the data, and noticing missing values plays an important role in feature engineering, as they say, “garbage in, garbage out”.
2) It requires domain expertise
Domain knowledge is all about subject matter expertise within the context of the use case. Understanding the industry of the machine learning project is essential to pinpoint which features are relevant and valuable, and to visualize how the data points might interrelate in significant, predictive ways. For example, in a situation where products have multiple names, it is important to know which products are actually the same and should be grouped together for the algorithm to treat them the same. The practitioner needs to understand whether apparent correlations between data points might be meaningful, or whether they are just a coincidence. This requires understanding the data and the context well enough to know what the data is not telling you, and what external datasets might help fill in these gaps, and engineer new features.
The high demand for machine learning has produced a large pool of data scientists who have developed expertise in tools and algorithms but lack the experience and industry-specific domain knowledge that feature engineering requires. Subject matter expertise helps programs start with a clear understanding of the business objectives and related measures of model performance and effectiveness.
3) It is time-consuming & resource-intensive
Feature engineering is essential for getting the most value out of your precious data, but doing it all by hand is time-consuming. Chances are that by the time the machine learning model is ready to deploy, the data might already be out of date.
The manual feature engineering process requires the data scientist to look at all of the data on hand and come up with possible combinations of columns and predictors that could provide the insights required to solve the business problem. Each individual feature needs to be tested in order to understand how they connect.
Feature engineering is an ongoing process, teams must continuously look for more effective features and models. In order to be successful, this work must be done inside a methodical and repeatable framework. Feature engineering is iterative – it involves testing, adjusting, and refining new features. The optimization loop in this process sometimes results in removal of low performing existing features or replacement using close variants until the highest impact features are identified. This could take weeks or even months, which might be fine for small projects, but isn’t scalable to large data science projects.
4) There are multiple techniques
Successful artificial intelligence and machine learning rely on model diversity, and therefore successful applications require the training of multiple algorithms – each potentially requiring different feature engineering techniques. The techniques to use will depend on the problem, the dataset, and the model; there is no one method that solves all feature engineering problems.
Feature engineering techniques are beyond the scope of this article, but the some of the common techniques are:
- Handling Outliers
- Log Transform
- One-Hot Encoding
- Grouping Operations
- Feature Split
- Extracting Date
Check out this article to learn more about the different feature engineering techniques. It goes over the methods for comprehensive data preprocessing with Pandas (including basic Python scripts/tutorials).
5) Doing it manually can lead to errors and biases
Even though domain knowledge is essential to interpret data relevant to a specific context, it can also have a blinkering effect. Given this approach’s subjectivity, it is common to overstate some connections between phenomena in the data based on what is already believed to be true, while disregarding other connections that may turn out to be fruitful. This is a major challenge with manual feature engineering. It’s limited by what the data scientist deems noteworthy, relevant, or what they personally consider to be a predictor. Features are limited to the scope of that one person’s creativity, expertise, scope, and bandwidth. Humans tend to use what they know, but ignore the unknown unknowns, meaning they can miss out on potentially great features.
Is automated feature engineering the solution?
The goal of automated feature engineering for machine learning models is to help the data scientist by automatically creating many candidate features out of a dataset, of which the best ones can be selected and used for model training. Automated feature engineering produces a huge number of options based on every correlation the system can find. The automation process works in a similar way to manual feature engineering, but it extends the concept to as many connections as possible. Automated feature engineering leverages the use of artificial intelligence to automatically extract the best features (predictive values) to answer the questions the machine learning algorithms are trying to answer. This will speed up the feature engineering process, in addition to teasing out connections that might not be obvious to a human analyst. A good automated feature engineering tool suggests potential new features and will assess, test, and rank thousands of options by relevance and usefulness. This list provides the options that are best for the use case. Automated feature engineering streamlines the entire process and removes many barriers to finding the right features efficiently. The result is significantly more feature options than could have possibly been created manually. It can help rank features not only by which ones work as the best predictors, but also by which ones are the most relevant in the context of the business problem. This gives scope to experiment with different predictors and features without piling on more workload, providing better answers quickly, and reducing the time to model deployment.
When it comes to leveraging automated feature engineering, there are 3 main options:
- Use open-source Python libraries for automated feature engineering such as featuretools. Featuretools uses an algorithm called deep feature synthesis to generate feature sets for structured datasets.
- There are also AutoML platforms that offer automated feature engineering.
- An External Data Platform (such as Explorium’s) that provides built-in automated feature engineering.
It’s time to build more accurate predictive models
Feature engineering is an integral part of every machine learning application because created and selected features have a great impact on model performance. Features that are relevant to the problem at hand, and appropriate for the model, will boost model accuracy. For the reasons listed above, it can be a challenging and time-consuming process. Best-practice feature engineering requires skills and subject matter expertise that take years to acquire. In addition, data access presents its own set of challenges, it can be difficult to find the right data needed for a specific business problem. Luckily, today there are platforms, such as external data platforms, that help solve these major challenges by providing access to relevant data and automated feature engineering solutions.
Explorium provides the first External Data Platform to improve Analytics and Machine Learning. Explorium enables organizations to automatically discover and use thousands of relevant data signals to improve predictions and ML model performance. Explorium External Data Platform empowers data scientists and analysts to acquire and integrate third-party data efficiently, cost-effectively and in compliance with regulations. With faster, better insights from their models, organizations across fintech, insurance, consumer goods, retail and e-commerce can increase revenue, streamline operations and reduce risks. Learn more at www.explorium.ai.