[scikit-learn] GradientBoostingRegressor, question about initialisation with MeanEstimator
aadral at gmail.com
Sun Aug 28 04:57:28 EDT 2016
I was looking exactly for this article. Thank you very much.
2016-08-28 5:30 GMT+01:00 Mathieu Blondel <mathieu at mblondel.org>:
> This comes from Algorithm 1, line 1, in "Greedy Function Approximation: a
> Gradient Boosting Machine" by J. Friedman.
> Intuitively, this has the same effect as fitting a bias (intercept) term
> in a linear model. This allows the subsequent iterations (decision trees)
> to work with centered targets.
> On Wed, Aug 24, 2016 at 5:24 AM, Алексей Драль <aadral at gmail.com> wrote:
>> 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
>> scikit-learn mailing list
>> scikit-learn at python.org
> scikit-learn mailing list
> scikit-learn at python.org
Alexey A. Dral
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