Solving business problems with "Prediction Engineering"
Translating a business need to a technical prediction problem definition often requires many iterations. While use cases are similar, the data and processes required to take advantage of predictive models can vary between large enterprises. Feature Labs offers a flexible interface to define and predict any future "event" of interest. After building a simple predictive model, the prediction can be refined to answer questions like:
Automated Feature Engineering
Most machine learning companies expect data to appear cleanly formatted as a single table, where one column is used to predict all of the others. However, many datasets are actually observations collected over time and do not fit this format. In order to make future predictions, patterns from our historical data must be extracted. Feature Labs does this automatically for any relational and time series dataset using our proprietary algorithm called Deep Feature Synthesis. This enables users to obtain human data scientist accuracy levels in 1/10th of the time.
Easy Model Deployment
Return on investment only occurs when organizations operationalize and integrate their developed predictive models. Feature Labs was built to make it possible to effortlessly transition from exploratory work to production. Once your work is in production, it's easy to retrain using new data, adjust modeling parameters, and confidently evaluate performance.