ee.ConfusionMatrix

Creates a confusion matrix. Axis 0 (the rows) of the matrix correspond to the actual values, and Axis 1 (the columns) to the predicted values.

Usage	Returns
`ee.ConfusionMatrix(array, order)`	ConfusionMatrix

Argument	Type	Details
`array`	Object	A square, 2D array of integers, representing the confusion matrix. Note that unlike the ee.Array constructor, this argument cannot take a list.
`order`	List, default: null	The row and column size and order, for non-contiguous or non-zero based matrices.

Examples

Code Editor (JavaScript)

// A confusion matrix. Rows correspond to actual values, columns to
// predicted values.
var array = ee.Array([[32, 0, 0,  0,  1, 0],
                      [ 0, 5, 0,  0,  1, 0],
                      [ 0, 0, 1,  3,  0, 0],
                      [ 0, 1, 4, 26,  8, 0],
                      [ 0, 0, 0,  7, 15, 0],
                      [ 0, 0, 0,  1,  0, 5]]);
print('Constructed confusion matrix',
      ee.ConfusionMatrix(array));

// The "order" parameter refers to row and column class labels. When
// unspecified, the class labels are assumed to be a 0-based sequence
// incrementing by 1 with a length equal to row/column size.
print('Default row/column labels (unspecified "order" parameter)',
      ee.ConfusionMatrix({array: array, order: null}).order());

// Set the "order" parameter when custom class label integers are required. The
// list of integer value labels should correspond to the matrix axes left to
// right / top to bottom.
var order = [11, 22, 42, 52, 71, 81];
print('Specified row/column labels (specified "order" parameter)',
      ee.ConfusionMatrix({array: array, order: order}).order());

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

from pprint import pprint

# A confusion matrix. Rows correspond to actual values, columns to
# predicted values.
array = ee.Array([[32, 0, 0,  0,  1, 0],
                  [ 0, 5, 0,  0,  1, 0],
                  [ 0, 0, 1,  3,  0, 0],
                  [ 0, 1, 4, 26,  8, 0],
                  [ 0, 0, 0,  7, 15, 0],
                  [ 0, 0, 0,  1,  0, 5]])
print('Constructed confusion matrix:')
pprint(ee.ConfusionMatrix(array).getInfo())

# The "order" parameter refers to row and column class labels. When
# unspecified, the class labels are assumed to be a 0-based sequence
# incrementing by 1 with a length equal to row/column size.
print('Default row/column labels (unspecified "order" parameter):',
      ee.ConfusionMatrix(array, None).order().getInfo())

# Set the "order" parameter when custom class label integers are required. The
# list of integer value labels should correspond to the matrix axes left to
# right / top to bottom.
order = [11, 22, 42, 52, 71, 81]
print('Specified row/column labels (specified "order" parameter):',
      ee.ConfusionMatrix(array, order).order().getInfo())