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1. github.com › LAMDA-NJU › Deep-ForestLAMDA-NJU/Deep-Forest - GitHub

   github.com › LAMDA-NJU › Deep-Forest

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   1 Φεβ 2021 · DF21 is an implementation of Deep Forest 2021.2.1. It is designed to have the following advantages: Powerful: Better accuracy than existing tree-based ensemble methods. Easy to Use: Less efforts on tunning parameters. Efficient: Fast training speed and high efficiency. Scalable: Capable of handling large-scale data.

2. pypi.org › project › deep-forestdeep-forest · PyPI

   pypi.org › project › deep-forest

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   DF21 is an implementation of Deep Forest 2021.2.1. It is designed to have the following advantages: Powerful: Better accuracy than existing tree-based ensemble methods. Easy to Use: Less efforts on tunning parameters. Efficient: Fast training speed and high efficiency. Scalable: Capable of handling large-scale data.

3. github.com › topics › deep-forestdeep-forest · GitHub Topics · GitHub

   github.com › topics › deep-forest

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   3 Φεβ 2021 · A model to predict presence of Heart Disease using Deep Forest Model (Cascaded Random Forest), KNN, Naive Bayes, and SVM.

4. deep-forest.readthedocs.io › en › stableDF21 Documentation — Deep Forest (DF21) documentation - Read the...

   deep-forest.readthedocs.io › en › stable

   • Αποθηκευμένο αντίγραφο

   1 Φεβ 2021 · DF21 is an implementation of Deep Forest 2021.2.1. It is designed to have the following advantages: Powerful: Better accuracy than existing tree-based ensemble methods. Easy to Use: Less efforts on tunning parameters. Efficient: Fast training speed and high efficiency. Scalable: Capable of handling large-scale data.

5. github.com › weecology › DeepForestPython Package for Airborne RGB machine learning - GitHub

   github.com › weecology › DeepForest

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   DeepForest is a python package for training and predicting ecological objects in airborne imagery. DeepForest currently comes with a tree crown object detection model and a bird detection model. Both are single class modules that can be extended to species classification based on new data.

6. medium.com › hub-by-littlebigcode › diversity-and-deep-forest-theory-applying-anDiversity and Deep Forest theory: Applying an Important ... -...

   medium.com › hub-by-littlebigcode › diversity-and-deep-forest-theory-applying-an

   14 Ιουν 2022 · The revolutionary deep forest frameworks enable the introduction of diversity as the tip of the iceberg. The following article will present the framework and diversity, as well as demonstrate...

7. deep-forest.readthedocs.io › _ › downloadsDeep Forest (DF21) - Read the Docs

   deep-forest.readthedocs.io › _ › downloads

   It is designed to have the following advantages: • Powerful: Better accuracy than existing tree-based ensemble methods. • Easy to Use: Less efforts on tunning parameters. • Efficient: Fast training speed and high efficiency. • Scalable: Capable of handling large-scale data.