Wikis
Info-nuggets to help anyone understand various concepts of MLOps, their significance, and how they are managed throughout the ML lifecycle.
Accuracy
Machine learning model accuracy is the measurement used to discover which model is best at recognising correlations and patterns between variables in a dataset.
Machine learning model accuracy is the measurement used to discover which model is best at recognising correlations and patterns between variables in a dataset. The measurement considers input or training datasets. The formula to calculate accuracy is:
Accuracy = TP+TN / TP+TN+FP+FN
Where:
- False negative:
False Negatives (FN) are negative outcomes that the model predicted incorrectly
- False positive
False Positives (FP) are positive outcomes that the model predicted incorrectly
- True positive
True positives (TP) are positive outcomes that the model predicted correctly.
- True negative
True Negatives (TN) are negative outcomes that the model predicted correctly.
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