The accuracy metric measures how correct your model predictions are by calculating the proportion of correct results among the total number of results.
Metric details
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Accuracy is a quality evaluation metric that measures the quality of the performance of binary and multiclass classification machine learning models in watsonx.governance.
Scope
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The accuracy metric evaluates machine learning models only.
Types of AI assets: Machine learning models
Machine learning problem type:
Binary classification
Multiclass classification
Scores and values
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The accuracy metric score indicates the proportion of correct predictions that are made by your model when compared to the total number of predictions. Higher scores indicate that more correct predictions are made.
Range of values: 0.0-1.0
Best possible score: 1.0
Chart values: Last value in the timeframe
Ratios:
At 0: no correct predictions
Over 0: indicates increasing accuracy with more correct predictions
Settings
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Default threshold: Lower limit = 80%
Do the math
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Accuracy is calculated with the following formula: