Happy New year!!!

Every year we commit for new resolution to ourself, How deep learning measure the accuracy, precision , senstivity of them

In this episode , I covered following performance metrics of Machine learning

 Confusion Matrix:It is the easiest way to measure the performance of a classification problem where the output can be of two or more type of classes. A confusion matrix is nothing but a table with two dimensions viz. “Actual” and “Predicted” and furthermore, both the dimensions have “True Positives (TP)”, “True Negatives (TN)”, “False Positives (FP)”, “False Negatives (FN)”

It is most common performance metric for classification algorithms. It may be defined as the number of correct predictions made as a ratio of all predictions made. We can easily calculate it by confusion matrix with the help of following formula −

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

Precision, used in document retrievals, may be defined as the number of correct documents returned by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

Precision=TP/TP+FP

Recall may be defined as the number of positives returned by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

Recall=TP/TP+FN

Specificity, in contrast to recall, may be defined as the number of negatives returned by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

Specificity=TN/TN+FP

This score will give us the harmonic mean of precision and recall. Mathematically, F1 score is the weighted average of the precision and recall. The best value of F1 would be 1 and worst would be 0. We can calculate F1 score with the help of following formula −

𝑭𝟏 = 𝟐 ∗ (𝒑𝒓𝒆𝒄𝒊𝒔𝒊𝒐𝒏 ∗ 𝒓𝒆𝒄𝒂𝒍𝒍) / (𝒑𝒓𝒆𝒄𝒊𝒔𝒊𝒐𝒏 + 𝒓𝒆𝒄𝒂𝒍𝒍)

F1 score is having equal relative contribution of precision and recall.

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