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 with multiclass.
• multiclass of integers is more specific but compatible with continuous.
• multilabel-indicator is more specific but compatible with multiclass-multioutput.

Parameters:

y : array-like

Returns:

target_type : string 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]])
'multiclass-multioutput'
>>> 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'