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Model map on Zoo dataset

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Build Model Map

class Orange.modelmaps.BuildModelMap(fname, folds=10, model_limit=500, seed=42)
build_model(attributes, learner)

Build a classification meta-model.

  • attributes (list of strings) – subset of attributes
  • learner (Orange.classification.Learner) – classification learner to wrap

Return an of model meta-data.

build_model_matrix(models, dist=<function distance_manhattan at 0x3735b90>)

Build a distance matrix of models given the distance measure.

build_projection_model(attributes, viz_method)

Build a projection meta-model.

  • attributes (list of strings) – attributes for projection
  • viz_method (enum) – visualization method
build_rf_models(trees=50, max_depth=4, min_instances=5)

Build Random forest and return tree models.

  • trees (int) – number of trees in the forest
  • max_depth (int) – maximal tree depth
  • min_instances (int) – nodes with less than min_instances instances are not split further
select_representatives(models, dist=<function distance_euclidean at 0x3735c08>)

Contruct a network, detect communities and return representatives.

  • models (list of modelmaps.Model) – select representatives from this models
  • dist (func) – distance function

Help Functions


Load model map.

Read compressed tuple containing model similarity matrix and data table., smx, model_data, original_data)

Save model map.

Model similarity matrix and data table tuple is pickled and compressed as a bz2 archive.


Return an empty data table for model meta data.

Orange.modelmaps.modelmap.get_feature_subsets(domain, nsubsets=None, min_features=None, max_features=None, seed=None)

Return random attribute subsets.

  • domain ( – data set domain to extract features
  • nsubsets (int) – number of attribute subsets
Orange.modelmaps.modelmap.model_network(smx, table=None, knn=1)

Build network from model distance matrix.

  • smx (Orange.misc.SymMatrix) – model distance matrix
  • table ( – model meta data
  • knn (int) – connect each model with knn neighbours