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Computes a 2D error matrix for a collection by comparing two columns of a collection: one containing the actual values, and one containing predicted values. The values are expected to be small contiguous integers, starting from 0. Axis 0 (the rows) of the matrix correspond to the actual values, and Axis 1 (the columns) to the predicted values.
The name of the property containing the actual value.
predicted
String
The name of the property containing the predicted value.
order
List, default: null
A list of the expected values. If this argument is not specified, the values are assumed to be contiguous and span the range 0 to maxValue. If specified, only values matching this list are used, and the matrix will have dimensions and order matching this list.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-09-19 UTC."],[[["Computes a 2D error matrix (confusion matrix) for a FeatureCollection by comparing actual and predicted values."],["Takes the names of the properties containing the actual and predicted values as inputs."],["Accepts an optional 'order' argument to specify the expected values for the matrix axes."],["The matrix rows represent actual values and columns represent predicted values, aiding in assessing classification accuracy."],["Values are expected to be small, contiguous integers starting from 0."]]],["The `errorMatrix` method computes a 2D confusion matrix by comparing actual and predicted values from two columns within a FeatureCollection. It takes `actual` and `predicted` column names as inputs, and an optional `order` list to define the matrix's dimensions and included values. The function uses small contiguous integers starting from 0, and returns a `ConfusionMatrix` object that includes overall accuracy, consumer's accuracy, producer's accuracy and kappa.\n"]]