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.
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