pipefitter.estimator.GBTree.fit

GBTree.fit(table, *args, **kwargs)

Fit function for gradient boosting tree

Parameters:

*args : dicts or two-element tuples or consecutive key/value pairs, optional

The following types are allowed:
  • Dictionaries contain key/value pairs of parameters.
  • Two-element tuples must contain the name of the parameter in the first element and the value in the second element.
  • Consecutive key/value pairs are also allowed.

**kwargs : keyword arguments, optional

These keyword arguments are the same as on the constructor.
Returns:

GBTreeModel

Examples

>>> gbt = GBTree(target='Origin',
...              inputs=['MPG_City', 'MPG_Highway', 'Length',
...                      'Weight', 'Type', 'Cylinders'],
...              nominals = ['Type', 'Cylinders', 'Origin'])
>>> model = gbt.fit(training_data)