turicreate.nearest_neighbor_classifier.NearestNeighborClassifier.predict

NearestNeighborClassifier.predict(dataset, max_neighbors=10, radius=None, output_type='class', verbose=True)

Return predicted class labels for instances in dataset. This model makes predictions based on the closest neighbors stored in the nearest neighbors classifier model.

Parameters:
dataset : SFrame

Dataset of new observations. Must include the features used for model training, but does not require a target column. Additional columns are ignored.

max_neighbors : int, optional

Maximum number of neighbors to consider for each point.

radius : float, optional

Maximum distance from each point to a neighbor in the reference dataset.

output_type : {‘class’, ‘probability’}, optional

Type of prediction output:

  • class: Predicted class label. The class with the maximum number of votes among the nearest neighbors in the reference dataset.
  • probability: Maximum number of votes for any class out of all nearest neighbors in the reference dataset.
Returns:
out : SArray

An SArray with model predictions.

See also

create, classify, predict_topk

Notes

  • If the ‘radius’ parameter is small, it is possible that a query point has no qualified neighbors in the training dataset. In this case, the result for that query is ‘None’ in the SArray output by this method. If the target column in the training dataset has missing values, these predictions will be ambiguous.
  • Ties between predicted classes are broken randomly.

Examples

>>> sf_train = turicreate.SFrame({'species': ['cat', 'dog', 'fossa', 'dog'],
...                             'height': [9, 25, 20, 23],
...                             'weight': [13, 28, 33, 22]})
...
>>> sf_new = turicreate.SFrame({'height': [26, 19],
...                           'weight': [25, 35]})
...
>>> m = turicreate.nearest_neighbor_classifier.create(sf, target='species')
>>> ystar = m.predict(sf_new, max_neighbors=2, output_type='class')
>>> print ystar
['dog', 'fossa']