Re: [scikit-learn] Scikit-learn got a prize in France

I literally owe my career in the data space to scikit-learn. It’s not just a framework but a school of thought regarding predictive modeling. Super well deserved, folks :) Schots Em sáb., 5 de fev. de 2022 às 13:32, Gyro Funch escreveu: > On 2022-02-05 04:23 PM, Gael Varoquaux wrote: > > Hi ev

Re: [scikit-learn] Regarding negative value of sklearn.metrics.r2_score and sklearn.metrics.explained_variance_score

There is no constraint, that’s the point since nothing limits you to have a model with crap predictions leading to be worse than to just predict the target’s mean for every data point. If you do so —> negative R2. Best Regards, Em qui., 12 de ago. de 2021 às 16:21, Samir K Mahajan < samirkmahaja

Re: [scikit-learn] sample_weight vs class_weight

I have been using both in time-series classification. I put a exponential decay in sample_weights AND class weights as a dictionary. BR/Schots Em sex., 4 de dez. de 2020 às 12:01, Nicolas Hug escreveu: > Basically passing class weights should be equivalent to passing > per-class-constant sample

Re: [scikit-learn] Issue with Sklearn.Logistic Regression

You should instantiate LogisticRegression() before fitting. logreg = LogisticRegression().fit(Xnp,ynp) []’s Maykon Schots Em dom., 1 de nov. de 2020 às 23:41, The Helmbolds via scikit-learn < scikit-learn@python.org> escreveu: > What parentheses? > Enclosing what? > > "You won't find the right