Validation Metrics

Metrics to evaluate the performance of an Extreme learning machine for regression and classification tasks.

mse(y, ŷ)

Calculate the mean squared error

See also mae.

Examples

julia> mse([0.0, 0.0, 0.0], [0.0, 0.0, 0.0])
0
julia> mse([-1.0, -1.0, -1.0], [1.0, 1.0, 1.0])
4

mae(y, ŷ)

Calculate the mean absolute error

See also mse.

Examples

julia> mae([-1.0, -1.0, -1.0], [1.0, 1.0, 1.0])
2
julia> mae([1.0, 1.0, 1.0], [2.0, 2.0, 2.0])
1

confusionmatrix(y, ŷ)

Generate a confusion matrix

Examples

julia> confusionmatrix([1, 1, 1, 1, 0], [1, 1, 1, 1, 0])
2×2 Matrix{Int64}:
 1  0
 0 4
julia> confusionmatrix([1, 1, 1, 1, 0, 2], [1, 1, 1, 1, 0, 2])
3×3 Matrix{Int64}:
 1  0 0
 0 4 0
 0 0 1

accuracy(y, ŷ)

Calculate the accuracy for a classification task

Examples

julia> accuracy([1, 1, 1, 1], [0, 1, 1, 0])
0.5
julia> accuracy([1, 2, 3, 4], [1, 1, 1, 1])
0.25

precision(y, ŷ)

Calculate the precision for a classification task

See also recall.

Examples

julia> precision([0, 1, 0, 0], [0, 1, 1, 0])
0.5
julia> precision([0, 1, 0, 0], [0, 1, 0, 0])
1

recall(y, ŷ)

Calculate the recall for a classification task

See also precision.

Examples

julia> recall([1, 2, 1, 3, 0], [2, 2, 2, 3, 1])
0.5
julia> recall([1, 2, 1, 3, 2], [2, 2, 2, 3, 1])
1

F1(y, ŷ)

Calculate the F1 score for a classification task

Examples

julia> F1([1, 2, 1, 3, 0], [2, 2, 2, 3, 1])
0.4
julia> F1([1, 2, 1, 3, 2], [2, 2, 2, 3, 1])
0.47058823529411764