Re: [Scikit-learn-general] Comparisons of classifiers

Oh, I see. I think random forest is just a different approach … I would say that xgboost is kind of a hybrid algorithm borrowing ideas from random forests and boosting. Random forests, Adaboost, xgboost, etc. are just different algorithms (like logistic regression, SVMs, and multi-layer

Re: [Scikit-learn-general] Comparisons of classifiers

On Friday, 25 March 2016, Sebastian Raschka wrote: > > wondering what changes are needed to make > > RandomForestClassifier competitive with xgboost and H20 at > > Do you mean in terms of predictive performance (not computational > efficiency)? Not sure what other's think,

Re: [Scikit-learn-general] scikit-learn PDF Documentation File Not Found

Hi, I am not sure whether these are the ones needed but you can check the dev docs . There is a link to the printable pdf version there which work. Cheers, Maniteja ___

[Scikit-learn-general] scikit-learn PDF Documentation File Not Found

Hello, I want to download scikit-learn PDF document. So, I browsed the webpage: http://scikit-learn.org/stable/index.html I tried to download the PDF document at here: In the top of the webpage -> Documentation -> PDF documentation Unfortunately, the webpage showed an error message: File Not

Re: [Scikit-learn-general] Binary Classifier Evaluation Metrics

It looks like you should use the http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html to convert y_train into a binary indicator matrix format that scikit-learn can work with. On 25 March 2016 at 18:42, Enise Basaran wrote: >