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CTO of Mattermark here. It is a really interesting problem, because as you say even if you boost the odds 25x they're still really low. We trained the data se
by kevin_morrill 13y ago
CTO of Mattermark here. It is a really interesting problem, because as you say even if you boost the odds 25x they're still really low. We trained the data set on venture backed founders (e.g. Series A or beyond), which is a bit higher bar than just any founder. The hope being that once you reach Series A you're less likely to fail than just having seed funding. At some point we want to go back and look at what differentiates founders that reach seed vs. venture backing.
- JasonCEC 13y agoCan you talk a bit about your feature selection or models? I run a statistical quality control company using machine learning, and picking up on flaws with tiny probabilities (one batch in every twenty or thirty million) might benefit from similar techniques!