Re: [scikit-learn] Commercial use of ML algorithms and scikit-learn

Licensing and patents are orthogonal. They are pretty much unrelated. In terms of the license, you can do with the code whatever you like. If any of the algorithms were (are?) patented, independent of the implementation, you would have to pay a license fee to use it - no matter if you use a comm

[scikit-learn] Confidence interval estimation for probability estimators

Let's say I have a base estimator that predicts the likelihood of an binary (Bernoulli) outcome: model.fit(X, y) where y contains [0 or 1] P = model.predict(X)/predict_proba(X) give values in the range [0 to 1] (model here might be a calibrated LogisticRegression model). Is there a way to est

Re: [scikit-learn] Accessing Clustering Feature Tree in Birch

Hi Roman, Thank you for the detailed and informative answer. On Mon, Oct 2, 2017 at 12:14 PM, Roman Yurchak wrote: > Hello, > > sklearn.cluster.Birch follows the original BIRCH paper, that appears to be > mostly focused on efficiently building the hierarchical clustering tree > (and not so much