thanks to all of you. I think I have got the point. ^_^ 2016-09-13 20:30 GMT+08:00 Dale T Smith <[email protected]>:
> Wrong! Apologies, I had a double loop in there. > > > > Get a random sample of the training data > > For I to n_estimators: > > Build a tree – this involves a *random sample of features* > and thresholds for each feature in the training data sample at each node. > > Use the rest of the training data, not in the sample, to > calculate the out-of-bag score. > > > > I also edited a bit for clarity. Refer to Gilles Loope’s dissertation for > details. > > > > ____________________________________________________________ > ______________________________ > *Dale Smith* | Macy's Systems and Technology | IFS eCommerce | Data > Science > 770-658-5176 | 5985 State Bridge Road, Johns Creek, GA 30097 | > [email protected] > > > > *From:* scikit-learn [mailto:scikit-learn-bounces+dale.t.smith= > [email protected]] *On Behalf Of *Dale T Smith > *Sent:* Tuesday, September 13, 2016 8:24 AM > *To:* Scikit-learn user and developer mailing list > *Subject:* Re: [scikit-learn] is RandomForest random samples or random > features? > > > > ⚠ EXT MSG: > > Each tree is built using a random sample with replacement from the > provided training data. The data not in the sample is used to calculate the > out-of-bag score. The “bag” is the sampled data. > > > > The “random” refers to several features of the algorithm, including random > sampling of features > > > > So for each tree > > Get a random sample of the training data > > For I to n_estimators: > > Build a tree – this involves a *random > sample of features* and thresholds for each feature in the sample at each > node. > > Use the rest of the training data, not in > the sample, to calculate the out-of-bag score > > > > Random Forest already incorporates “random features”. > > > > https://github.com/glouppe/phd-thesis > > > > ____________________________________________________________ > ______________________________ > *Dale Smith* | Macy's Systems and Technology | IFS eCommerce | Data > Science > 770-658-5176 | 5985 State Bridge Road, Johns Creek, GA 30097 | > [email protected] > > > > *From:* scikit-learn [mailto:scikit-learn-bounces+ > [email protected] > <[email protected]>] *On Behalf Of * > ?? > *Sent:* Tuesday, September 13, 2016 4:16 AM > *To:* [email protected] > *Subject:* [scikit-learn] is RandomForest random samples or random > features? > > > > ⚠ EXT MSG: > > I have read the Guide of sklearn's RandomForest : > > """ > In random forests (see RandomForestClassifier > <http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier> > and RandomForestRegressor > <http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor> > classes), each tree in the ensemble is built from a sample drawn with > replacement (i.e., a bootstrap sample) from the training set. > """ > > But I prefer RandomForest as : > """ > features ("attributes", "predictors", "independent variables") are > randomly sampled > """ > > is RandomForest random samples or random features? where can I find a > features random version of RandomForest? > > thx. > > * This is an EXTERNAL EMAIL. Stop and think before clicking a link or > opening attachments. > > * This is an EXTERNAL EMAIL. Stop and think before clicking a link or > opening attachments. > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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