Information Theoretic Metrics for Multi-Class Predictor Evaluation
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Information Theoretic Metrics for Multi-Class Predictor Evaluation
1–6 of 6 posts
Re: Information Theoretic Metrics for Multi-Class Predictor Evaluation
#2I'm not entirely clear on why this was needed - The Matthews Correlation Coefficient can easily be used in multi-class multi-label classification and regression scenarios.
Re: Information Theoretic Metrics for Multi-Class Predictor Evaluation
#3I'm not entirely clear on why this was needed - The Matthews Correlation Coefficient can easily be used in multi-class multi-label classification and regression scenarios.
I'm guessing here -- because there are highly dependent uncorrelated datasets?
It's easy to give examples:
https://en.wikipedia.org/wiki/Normally_distributed_and_uncor...
Re: Information Theoretic Metrics for Multi-Class Predictor Evaluation
#4I'm not entirely clear on why this was needed - The Matthews Correlation Coefficient can easily be used in multi-class multi-label classification and regression scenarios.
[deleted]
Re: Information Theoretic Metrics for Multi-Class Predictor Evaluation
#5I'm not entirely clear on why this was needed - The Matthews Correlation Coefficient can easily be used in multi-class multi-label classification and regression scenarios.
https://en.wikipedia.org/wiki/Matthews_correlation_coefficie... is defined for binary (two-class) classifications only.
Re: Information Theoretic Metrics for Multi-Class Predictor Evaluation
#6I'm not entirely clear on why this was needed - The Matthews Correlation Coefficient can easily be used in multi-class multi-label classification and regression scenarios.
https://en.wikipedia.org/wiki/Matthews_correlation_coefficie... is defined for binary (two-class) classifications only.
MCC is trivially extended to support regression and multi-label. See the implementation in http://grey.colorado.edu/emergent