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. The measurement considers input or training datasets. The formula to calculate accuracy is:

Accuracy = TP+TN / TP+TN+FP+FN

Where:

1. False negative: 

           False Negatives (FN) are negative outcomes that the model predicted incorrectly

2. False positive

            False Positives (FP) are positive outcomes that the model predicted incorrectly

3. True positive

            True positives (TP) are positive outcomes that the model predicted correctly.

4. True negative

            True Negatives (TN) are negative outcomes that the model predicted correctly.