In our latest Cyclic Boosting release https://t.co/hsMftLtO2F, we introduce quantile regression (via pinball loss) and subsequent (requiring 3 predicted quantiles) fit-less estimation of full individual probability distributions by means of quantile-parameterized distributions.
We just open-sourced Cyclic Boosting, a pure-Python ML algorithm that's explainable, accurate, robust, easy to use, and fast! Learn more in our presentation #Cycl… @wickfelix at #PyConDE#PyDataBerlin
https://t.co/4O8kGZf7Zn
First open-source pre-release (still lots of polishing needed) of the Cyclic Boosting ML algorithms: https://t.co/mAWMRsnNL7
Feel free to try it out, simply do: pip install cyclic-boosting
Please come back with criticism and suggestions. Contributors highly welcome!
@antgoldbloom For products and locations, you need to find a way to deal with categorical features of high cardinality though. Simple one-hot encoding is not great and especially tree-based methods suffer here.
@antgoldbloom A single model used across all SKUs is not only better in terms of learning commonalities across SKUs, but also operationally way more convenient.
@antgoldbloom One of the most important, but usually overlooked, issues (especially for short-term forecasting) is temporal confounding. Autocorrelation is spurious and can mask true causal effects, e.g., from promotions or events, for the model. Take care how you include it.
Was it really just symmetry breaking via setting random initial weights what Rosenblatt missed to get backpropagation to work? (as described in Genius Makers)