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scores 2.0.0 documentation

Index to Documentation:

  • scores: Verification and Evaluation for Forecasts and Models
  • Key Features of scores
  • Detailed Installation Guide
  • Index of Metrics, Statistical Techniques and Data Processing Tools Included in scores
  • API Documentation
  • Contributing Guide
  • Data Sources
  • Tutorials
    • Data Fetching
    • Additive Bias, Multiplicative Bias and Percent Bias
    • MAE
    • RMSE
    • MSE
    • Pearson's Correlation
    • Kling-Gupta Efficiency
    • Quantile Loss
    • Murphy Diagrams
    • Flip-Flop Index
    • Consistent Scores
    • Threshold Weighted Scores
    • Quantile Interval Score and Interval Score
    • Brier Score
    • CRPS for forecasts expressed as CDFs
    • CRPS for ensemble forecasts
    • twCRPS for ensemble forecasts
    • Receiver Operating Characteristic (ROC)
    • FIRM
    • Binary (Categorical/Contingency/Confusion Matrix) Scores
    • Fractions Skill Score
    • Diebold Mariano
    • Isotonic Regression and Reliability Diagrams
    • Risk Matrix Score
    • Dimension Handling
    • Weighting Results
    • Angular Data
    • Introduction to Pandas API
  • Related Works
  • Maintainers Notes
  • Release Notes (What’s New)
  • .ipynb

Tutorials

Contents

  • Continuous
  • Probability
  • Categorical
  • Spatial
  • Statistical Tests
  • Processing
  • Emerging
  • Other
Interactive online version: Binder badge. Download notebook.

Tutorials#

Jupyter Notebooks for tutorials of scores.

Firstly run Data Fetching to fetch the data needed for some later notebooks

Data Fetching

Continuous#

Additive Bias, Multiplicative Bias and Percent Bias
MAE
RMSE
MSE
Pearson's Correlation
Kling-Gupta Efficiency
Quantile Loss
Murphy Diagrams
Flip-Flop Index
Consistent Scores
Threshold Weighted Scores
Quantile Interval Score and Interval Score

Probability#

Brier Score
CRPS for forecasts expressed as CDFs
CRPS for ensemble forecasts
twCRPS for ensemble forecasts
Receiver Operating Characteristic (ROC)

Categorical#

FIRM
Binary (Categorical/Contingency/Confusion Matrix) Scores

Spatial#

Fractions Skill Score

Statistical Tests#

Diebold Mariano

Processing#

Isotonic Regression and Reliability Diagrams

Emerging#

Risk Matrix Score

Other#

Dimension Handling
Weighting Results
Angular Data
Introduction to Pandas API

previous

Data Sources

next

Tutorial One - Data Preparation

Contents
  • Continuous
  • Probability
  • Categorical
  • Spatial
  • Statistical Tests
  • Processing
  • Emerging
  • Other

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© Copyright Licensed under Apache 2.0 - https://www.apache.org/licenses/LICENSE-2.0.