Most implementations are actually moving in the opposite direction. Previously, there was a tendency to look to aggregate words into phrases to better capture the "context" of a word. Now, most approaches are splitting words into sub-word parts or even characters. With networks that capture temporal relationships across tokens (as opposed to older, "bag of words" models), multi-word patterns can effectively be captured by attending to the temporal order of sub-word parts.
Strong Analytics | Chicago, IL | Full-time Data Scientists, Data Engineers | https://www.strong.io
We help companies integrate state-of-the-art machine learning into their products, internal tools, and infrastructure. We've designed, built, and deployed products in the automotive space, pharma, gaming, retail, tech, and many other verticals.
Requires an advanced degree (M.S./Ph.D.) in a quantitative science and 1+ years applying machine learning to real-world problems.
Survival modeling is exactly what's needed for these situations. It allows you to (a) consider censored data (i.e., active customers who you know stay for at least X months) and, (b) use flexible survival distributions beyond the standard exponential distribution assumed in the typical monthly churn rate calculations.
Source: Run a data science company and we work on a lot of customer lifecycle modeling projects with companies much younger than yours.
A quantile-based confidence interval from bootstrapping can yield a 100% confidence interval that does not contain 0, i.e., with 100% of cases positive/negative. But that does not (necessarily) mean that there is a 100% chance that the new version is better than the old one. Confidence intervals are not Bayesian credible intervals and cannot be treated as such. (That said, making some certain assumptions about the underlying model can in some times allow one to treat nonparametric bootstraps in such a way.)