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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