Tech Brief: How to Streamline Feature Engineering for Better Predictive Modeling Results

Find Shortcuts for Data Science’s Most Time-Consuming, Error-Prone Processes

One of the leading causes of underperforming predictive models is suboptimal feature engineering. That’s because feature selection demands a combination of ample source data, shrewd human judgment, and thorough evaluation, which collectively present many opportunities for error.

Through years of experience, deep data science expertise, and our massive consumer data repository, Mobilewalla has identified solutions that expedite and optimize feature engineering for more accurate predictive modeling results. This tech brief for data scientists explores the underlying challenges of feature selection and presents simple strategies for addressing them.

The Two-Pronged Feature Selection Solution

  • Data enrichment – the supplementation of internal databases for more precise and predictive feature selection
  • Pre-defined features – the identification of feature categories and instances shown to have broad horizontal predictiveness 

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