type_of_target (original) (raw)

sklearn.utils.multiclass.type_of_target(y, input_name='', raise_unknown=False)[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 withmulticlass-multioutput.

Parameters:

y{array-like, sparse matrix}

Target values. If a sparse matrix, y is expected to be a CSR/CSC matrix.

input_namestr, default=””

The data name used to construct the error message.

Added in version 1.1.0.

raise_unknownbool, default=False

If True, raise an error when the type of target returned bytype_of_target is "unknown".

Added in version 1.6.

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

from sklearn.utils.multiclass import type_of_target 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'