sklearn.ensemble.AdaBoostRegressor

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A sklearn.ensemble.AdaBoostRegressor is an AdaBoost Regression System within sklearn.ensemble module.

  • Context
    • Usage:
1) Import AdaBoost Regression System from scikit-learn : from sklearn.ensemble import AdaBoostRegressor
2) Create design matrix X and response vector Y
3) Create AdaBoost Regressor object: regressor_model=AdaBoostRegressor([base_estimator=None, n_estimators=50, learning_rate=1.0, loss=’linear’, random_state=None])
4) Choose method(s):


References

2017a

  • (Scikit Learn, 2017B) ⇒ http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html
    • QUOTE:class sklearn.ensemble.AdaBoostRegressor(base_estimator=None, n_estimators=50, learning_rate=1.0, loss=’linear’, random_state=None)

      An AdaBoost [1] regressor is a meta-estimator that begins by fitting a regressor on the original dataset and then fits additional copies of the regressor on the same dataset but where the weights of instances are adjusted according to the error of the current prediction. As such, subsequent regressors focus more on difficult cases.

      This class implements the algorithm known as AdaBoost.R2 [2].

      Read more in the User Guide.

2017c


  1. Y. Freund, and R. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting”, 1997
  2. T. Hastie, R. Tibshirani and J. Friedman, “Elements of Statistical Learning Ed. 2”, Springer, 2009.
  3. J. Zhu, H. Zou, S. Rosset, T. Hastie. “Multi-class AdaBoost”, 2009.
  4. H.Drucker. “Improving Regressors using Boosting Techniques”, 1997.