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- W2482419876 abstract "Extremely randomized quantile forests are an ensemble method which extends ordinary random forests with additional randomness and quantiles. In this work we checked its prediction accuracy with different success measures and execution speed. We also analyzed influences of various parameters and size of learning dataset on prediction performance and time needed for execution. We compared method with different success measures and execution time also with two methods that use quantiles for prediction and three methods that give prediction intervals. Extremely randomized quantile forests have been proved as competitive in terms of prediction strength with different measures and also in speed of execution." @default.
- W2482419876 created "2016-08-23" @default.
- W2482419876 creator A5000424684 @default.
- W2482419876 date "2016-05-11" @default.
- W2482419876 modified "2023-09-27" @default.
- W2482419876 title "Extremely randomized quantile forests" @default.
- W2482419876 hasPublicationYear "2016" @default.
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