sklearn.utils.multiclass
.type_of_target¶
- sklearn.utils.multiclass.type_of_target(y)[source]¶
Determine the type of data indicated by the target.
Note that this type is the most specific type that can be inferred. For example:
binary
is more specific but compatible withmulticlass
.multiclass
of integers is more specific but compatible withcontinuous
.multilabel-indicator
is more specific but compatible withmulticlass-multioutput
.
- Parameters
- yarray-like
- Returns
- target_typestr
One of:
‘continuous’:
y
is an array-like of floats that are not all integers, and is 1d or a column vector.‘continuous-multioutput’:
y
is a 2d array of floats that are not all integers, and both dimensions are of size > 1.‘binary’:
y
contains <= 2 discrete values and is 1d or a column vector.‘multiclass’:
y
contains more than two discrete values, is not a sequence of sequences, and is 1d or a column vector.‘multiclass-multioutput’:
y
is a 2d array that contains more than two discrete values, is not a sequence of sequences, and both dimensions are of size > 1.‘multilabel-indicator’:
y
is a label indicator matrix, an array of two dimensions with at least two columns, and at most 2 unique values.‘unknown’:
y
is array-like but none of the above, such as a 3d array, sequence of sequences, or an array of non-sequence objects.
Examples
>>> import numpy as np >>> type_of_target([0.1, 0.6]) 'continuous' >>> type_of_target([1, -1, -1, 1]) 'binary' >>> type_of_target(['a', 'b', 'a']) 'binary' >>> type_of_target([1.0, 2.0]) 'binary' >>> type_of_target([1, 0, 2]) 'multiclass' >>> type_of_target([1.0, 0.0, 3.0]) 'multiclass' >>> type_of_target(['a', 'b', 'c']) 'multiclass' >>> type_of_target(np.array([[1, 2], [3, 1]])) 'multiclass-multioutput' >>> type_of_target([[1, 2]]) 'multilabel-indicator' >>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]])) 'continuous-multioutput' >>> type_of_target(np.array([[0, 1], [1, 1]])) 'multilabel-indicator'