Feature engineering is the process of selecting, transforming, and extracting features (also known as variables or attributes) from raw data to build machine learning models. The quality and relevance of features can have a significant impact on the performance and accuracy of a model.
The goal of feature engineering is to create a set of features that are informative, relevant, and meaningful for a specific task. This involves domain knowledge, creativity, and statistical techniques to identify, transform, and combine features. For example, in a text classification task, features could be the frequency of specific words or the presence of certain patterns. In an image classification task, features could be the color or texture of specific regions of an image.
Feature engineering is an iterative process and requires expertise in the domain, as well as knowledge of the machine learning algorithms being used. A well-designed feature engineering process can help reduce overfitting, improve model performance, and increase interpretability.
From our experience, data discovery & manipulation consumes more than 90% of the time & resources spent in creating a batch or real-time decision process - be it predictive, prescriptive or reactive. Moreover, the data used during the data exploration or model training phase is not easily reproducible when it comes to using the same signals in production. This situation is often referred to as data-inquivalence.
Further, in real-time decisioning systems (such as fraud detection, stock market predictions etc.), a lot in terms of system design architecture & infrastructure needs to be invested to build a highly performant system that runs with high efficiency (millisecond latency, high throughput) & reliability.
DashML is designed to be intuitive. So, anyone with knowledge about the data can easily curate new signals out of the raw data. With our point-in-time data creation capability, the same signal can be simulated as of specific time-points in the past to be able to evaluate what-if scenarios. And once the signals are evaluated, apply them to future decision with our highly performant rules and ML scoring engine.
Assuming you have created & evaluated a set of signals and used DashML’s point-in-time data creation capability to build out a historical dataset, our rules engine capability allows you to use your domain knowledge & creativity to combine these signals into any set of criteria.
For e.g., in a credit lending scenario question such as how would my accounts receivable portfolio look like if I tweaked my decisioning criteria to something like below:
Once you have honed into a criteria, you can quickly create an API to call for a response based on the same criteria. We’d love to hear about your use case as you embark on your data, AI/ML journey. Please reach out to us using our contact us form.
DashML’s vision is to make evaluation & integration of 3rd party data as easy & seamless as possible. Our external data hooks will allow you to create historical perspectives and run what-if scenarios to evaluate vendor data. For e.g., in a fraud detection use case, what if I had IP or email reputation scores during the time of the transactions. Could these have been useful to optimize approval rates while decreasing fraud loss. Once evaluated, these additional 3rd party signals can be combined with your internal data signals to build powerful decisioning criteria or ML models for batch or real-time decisioning.
DashML incorporates robust security measures, including data encryption, secure access controls, and compliance with industry standards and regulations to ensure the confidentiality, integrity, and availability of your data and models.
To get started, contact us for a product demo, pricing information, and assistance with setting up the product in your organization. Our support team will also be available to help you with onboarding, training, and any technical questions you may have.