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Releases: LAMDA-NJU/Deep-Forest

v0.1.7

01 Oct 04:05
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Quick fix on numpy compatibility.

v0.1.6

17 Sep 16:25
eafa6ac
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v0.1.5

16 Apr 10:58
05cd74c
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This release includes:

  • Official support on Python 3.9
  • Bug fix for deep forest in the customized mode

v0.1.4

11 Mar 10:13
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Added

  • Add support on customized estimators (#48) @xuyxu
  • Add official support for ManyLinux-aarch64 (#47) @xuyxu

Fixed

  • Fix the prediction workflow with only one cascade layer (#56) @xuyxu
  • Fix inconsistency on predictor name (#52) @xuyxu
  • Fix accepted types of target for CascadeForestRegressor (#44) @xuyxu

Improved

v0.1.3

22 Feb 16:25
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Added

v0.1.2

11 Feb 03:31
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Added

  • Add CascadeForestRegressor for regression problem (#25) @tczhao

v0.1.1

07 Feb 08:03
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Added

  • Implement the get_forest() method for efficient indexing (#22) @xuyxu
  • Support class label encoding (#18) @NiMaZi
  • Support sample weight in fit() (#7) @tczhao
  • Add configurable predictor parameter (#9) @tczhao
  • Add base class BaseEstimator and ClassifierMixin (#8) @pjgao

Fixed

  • Fix accepted data types on the binner (#23) @xuyxu