In machine learning in order to judge the performance of the algorithm Precision and Recall are used. We will discuss Precision and recall later, but before that, we need to understand the four things which are very important in precision and recall.
These are true positive, false positive, false negative and true negative. We will start the discussion with the help of an example, suppose we have designed an algorithm which is supposed to predict correctly the event of a fire. So, when there will be fire and an alert alarm will set on. We will take four scenarios one by one.
Scenario 1: there is a fire and the alarm goes on in this case the algorithm is correctly able to identify the event and the action is also correct hence this is known as true positive. Another simplest way of understanding this is that the actual event has occurred and the prediction of the algorithm is also correct hence it is true positive.
Scenario 2: there is no fire and the alarm also does not ring. In this case, the event has not occurred and the algorithm is also able to correctly respond to the event and the action is also correct. The actual event has not occurred and the prediction of the algorithm is also correct in predicting that the event has not occurred hence it is true negative.
Scenario 3: there is no fire but the alarm rings. In this case, that event has not occurred but the algorithm has predicted that the event has occurred, hence it is a false positive. In simple terms here the algorithm has misclassified the event and hence it is a false positive.
Scenario 4: there is fire and the alarm does not ring. In this case, the event has occurred but the algorithm has not predicted it correctly. In simple terms, the event has actually occurred but the machine was unable to identify it correctly hence it is known as a false negative.
To sum up, the classification power of the algorithm depends on the amount of true classification the algorithm has done in terms of the number of true positive and true negative. The performance of the model is considered to be good.
Whereas misclassification happens when the number of false-positive and false negative is more and in this case, the model did not perform well.
In the next discussion will be taking up the issues of Precision and recall in machine learning.
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