ML0013 Accuracy

Accuracy in machine learning is a metric used to evaluate the performance of a model, particularly in classification tasks. It is the ratio of correct predictions to the total number of predictions made.
Mathematically, it’s defined as:

If a model correctly predicts the class for 99 out of 100 samples, its accuracy is 99%.

True Positives (TP): The model correctly predicts the positive class.
False Positives (FP): The model incorrectly predicts the positive class (it predicted positive, but it was negative)
True Negatives (TN): The model correctly predicts the negative class.
False Negatives (FN): The model incorrectly predicts the negative class (it predicted negative, but it was positive).