In marketing and customer analytics, a feature is another word for predictors. Within the world of machine learning technical purists, a feature is another word for an attribute, input, or independent variable.
Feature engineering is a process of preparing predictors to maximize their potential for machine learning models. The goal is to to improve classification accuracy by considering the limitations of the learning algorithms. For example, marketing teams desire insights that help them improve conversion rates while wasting less impressions on disinterested customers or prospects. The key to success lies in the classification accuracy of those actionable insights that drive a brand's strategic decisions.
Preparation involves working with raw data through various feature transformations, extractions, feature selection and handcrafting new features. I like to refer to these steps as the artisanal craft analysts develop over time. As analysts evolve their abilities, they begin to distance themselves as above-average or excellent modelers providing measurable business value relative to their peers.
Feature engineering requires spending a lot of time with data. This includes examining descriptive statistics and visualizations of input attributes. The process involves thinking about structures in the data, the underlying form of the problem, and how best to expose these features to predictive modeling algorithms. The success of this tedious human-driven process depends heavily on the domain and statistical expertise of the analyst.
The feature engineering process requires a good understanding of the size and the quality of the data in hand, the performance metric that's being optimized, and the machine learning algorithm that is used. Although feature engineering is one of the most time-consuming processes and requires many skill sets, it’s also one of the most rewarding aspects of the data science workflow. I highly recommend checking out these articles by Funda Güneş (SAS Principal Machine Learning Developer) in addition to this posting:
Good features not only boost your brand's ability to exploit predictive accuracy within customer experiences but also allow the flexibility to train simpler models. This, in turn, enables brands to understand and interpret models better. For example, by using good features, brands can choose to fit a more interpretable regression or decision tree model with fewer predictors as opposed to training a complex black-box model (which is hard to interpret) without sacrificing prediction accuracy.
Why automate feature engineering?
All the manual requirements of performing feature engineering have made automating the process a challenging task. At the same time, the proven research results have made automated feature engineering an exciting topic here at SAS. This breakthrough has the following advantages:
With that said, I invite you to view a video and technology demonstration that will address the following topics within SAS Customer Intelligence 360:
Learn more about how the SAS platform can be applied for marketing data management here.
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