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Predicting Features that Drive Retention using Heterogenous Supervised Models Ensembles

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  • Niculae, Stefan

Abstract

Making a decision on what to invest development time in is dif- ficult. Today’s market is so competitive that you cannot afford to focus on negligible product features. Based on reported customer behavior, I propose a ranking of the most important features with the help of statistics and machine learning. Following this advice leads to making informed decisions leading to good use of devel- opers’ time.

Suggested Citation

  • Niculae, Stefan, 2016. "Predicting Features that Drive Retention using Heterogenous Supervised Models Ensembles," Thesis Commons 378jp, Center for Open Science.
    • Handle: RePEc:osf:thesis:378jp
    DOI: 10.31219/osf.io/378jp
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