ErrorMetrics Types

This page documents all types defined in ErrorMetrics.jl, generated using get_type_docstring from OmniTools.jl.

ErrorMetric

ErrorMetric

Abstract type for error / performance metrics

ErrorMetric <: Any

Error-based Metrics

MSE

Mean Squared Error: Measures the average squared difference between predicted and observed values

MSE <: ErrorMetric <: Any

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NAME1R

Normalized Absolute Mean Error with 1/R scaling: Measures the absolute difference between means normalized by the range of observations

NAME1R <: ErrorMetric <: Any

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NMAE1R

Normalized Mean Absolute Error with 1/R scaling: Measures the average absolute error normalized by the range of observations

NMAE1R <: ErrorMetric <: Any

Nash-Sutcliffe Efficiency Metrics

NNSE

Normalized Nash-Sutcliffe Efficiency: Measures model performance relative to the mean of observations, normalized to [0,1] range

NNSE <: ErrorMetric <: Any

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NNSEInv

Inverse Normalized Nash-Sutcliffe Efficiency: Inverse of NNSE for minimization problems, normalized to [0,1] range

NNSEInv <: ErrorMetric <: Any

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NNSEσ

Normalized Nash-Sutcliffe Efficiency with uncertainty: Incorporates observation uncertainty in the normalized performance measure

NNSEσ <: ErrorMetric <: Any

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NNSEσInv

Inverse Normalized Nash-Sutcliffe Efficiency with uncertainty: Inverse of NNSEσ for minimization problems

NNSEσInv <: ErrorMetric <: Any

---

NSE

Nash-Sutcliffe Efficiency: Measures model performance relative to the mean of observations

NSE <: ErrorMetric <: Any

---

NSEInv

Inverse Nash-Sutcliffe Efficiency: Inverse of NSE for minimization problems

NSEInv <: ErrorMetric <: Any

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NSEσ

Nash-Sutcliffe Efficiency with uncertainty: Incorporates observation uncertainty in the performance measure

NSEσ <: ErrorMetric <: Any

---

NSEσInv

Inverse Nash-Sutcliffe Efficiency with uncertainty: Inverse of NSEσ for minimization problems

NSEσInv <: ErrorMetric <: Any

Correlation-based Metrics

NPcor

Normalized Pearson Correlation: Measures linear correlation between predictions and observations, normalized to [0,1] range

NPcor <: ErrorMetric <: Any

---

NPcorInv

Inverse Normalized Pearson Correlation: Inverse of NPcor for minimization problems

NPcorInv <: ErrorMetric <: Any

---

Pcor

Pearson Correlation: Measures linear correlation between predictions and observations

Pcor <: ErrorMetric <: Any

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Pcor2

Squared Pearson Correlation: Measures the strength of linear relationship between predictions and observations

Pcor2 <: ErrorMetric <: Any

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Pcor2Inv

Inverse Squared Pearson Correlation: Inverse of Pcor2 for minimization problems

Pcor2Inv <: ErrorMetric <: Any

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PcorInv

Inverse Pearson Correlation: Inverse of Pcor for minimization problems

PcorInv <: ErrorMetric <: Any

Rank Correlation Metrics

NScor

Normalized Spearman Correlation: Measures monotonic relationship between predictions and observations, normalized to [0,1] range

NScor <: ErrorMetric <: Any

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NScorInv

Inverse Normalized Spearman Correlation: Inverse of NScor for minimization problems

NScorInv <: ErrorMetric <: Any

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Scor

Spearman Correlation: Measures monotonic relationship between predictions and observations

Scor <: ErrorMetric <: Any

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Scor2

Squared Spearman Correlation: Measures the strength of monotonic relationship between predictions and observations

Scor2 <: ErrorMetric <: Any

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Scor2Inv

Inverse Squared Spearman Correlation: Inverse of Scor2 for minimization problems

Scor2Inv <: ErrorMetric <: Any

---

ScorInv

Inverse Spearman Correlation: Inverse of Scor for minimization problems

ScorInv <: ErrorMetric <: Any

All ErrorMetric Types

To list all available metric types and their purposes:

using ErrorMetrics
using OmniTools: show_methods_of

# Display all metric types
show_methods_of(ErrorMetric)