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Commits on Mar 15, 2019

• Merge pull request #3647 from janezd/pylint-owimpute

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Commits on Jan 25, 2019

• Merge pull request #3549 from AndrejaKovacic/keep_data_owtransform

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• Merge pull request #3532 from pavlin-policar/pca-for-sparse

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Commits on Nov 24, 2017

• Add nansum and nanmedian.

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Commits on Oct 3, 2017

• Do not fail in cases where data.Y is a column vector or 0-dimensional.

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Commits on Jun 2, 2017

• Venn diagram on sparse data (#2334)

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Commits on Jan 6, 2017

• Remove redundant lines and comments.

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• Extend checking for continuous attributes for cases on no columns or only meta columns.

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• Issue a warning if Low is greater than High and bypass updating colors.

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Commits on Jun 21, 2016

• Remove redundant signals. Remove set_preprocessor method. Set LEARNER to PolynomialLearner.

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• Patching PolynomialLearner.

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• Merging changes of previous two commits from master.

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• Add __returns__ attribute to PolynomialModel.

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Commits on May 13, 2016

• Added SameValue filter tests with Mock and disc, cont, meta variable types.

  

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• Remove invalid attribute inst._values

  

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• Code for test_filter_same_value to be moved in test_filter.py

  

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Commits on Aug 7, 2015

• Majority: Use consistent random class for ties

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Commits on Feb 16, 2015

• Code edit.

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• Silhouette plot generator - interactive plot.

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• Silhouette plot generator.

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Commits on Feb 13, 2015

• Hierarchical clustering returns the linkage matrix, required to plot dendrogram using scipy.

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Commits on Feb 10, 2015

• Evaluation of clustering algorithms - tests.

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• Code edit

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• Override constructors.

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• Evaluation of clustering algorithms.

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• DBSCAN.

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• Code edit.

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• KMeans clustering from Sklearn - tests.

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• KMeans clustering from Sklearn.

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Commits on Dec 12, 2014

• Warn if calling Scikit learn with DiscreteAttributes with more than 2 values.

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• Modified SklModel to return probabilities along with decisions.

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• Default sampling with percentage and default size is 70%.

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Commits on Dec 5, 2014

• Visualization for classification tree.

  

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• Flatten array to resolve bug.

  

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• Basic functionality of Classification tree visualization.

  

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