Re: [scikit-learn] Random Forest with Bootstrapping

Hi, >From docs http://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html : The RandomForestClassifier is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations z_i = (x_i, y_i). The out-of-bag (OOB) error is the aver

Re: [scikit-learn] suggested machine learning algorithm

2016-10-02 13:23 GMT+01:00 Thomas Evangelidis : > > > On 1 October 2016 at 20:48, Алексей Драль wrote: > >> Hi Thomas, >> >> What quality do you have on training? >> >> There is no silver bullet, but there is quite common technique you can >> use t

Re: [scikit-learn] suggested machine learning algorithm

Hi Thomas, What quality do you have on training? There is no silver bullet, but there is quite common technique you can use to find out if you use appropriate algorithm. You can take a look at the difference between "train" and "validation" quality of learning curves ( example

Re: [scikit-learn] GradientBoostingRegressor, question about initialisation with MeanEstimator

me effect as fitting a bias (intercept) term > in a linear model. This allows the subsequent iterations (decision trees) > to work with centered targets. > > Mathieu > > On Wed, Aug 24, 2016 at 5:24 AM, Алексей Драль wrote: > >> Hi there, >> >> I recently found

[scikit-learn] GradientBoostingRegressor, question about initialisation with MeanEstimator

Hi there, I recently found out that GradientBoostingRegressor uses MeanEstimator for the initial estimator in ensemble. Could you please point out (or explain) to the research showing superiority of this approach compared to the usage of DecisionTreeRegressor? -- Yours sincerely, Alexey A. Dral