Classification

Logistic Regression

Machine Learning

December 31, 2020

Why there is a need for other metrics if ‘accuracy’ is already present?

Honorable Madras High Court said that-“1000 Culprits Can Escape but, One Innocent Should Not be Punished “. More or less legal systems around the world follow this principle.

The above motto shows that accuracy is not always the metric that is been sought out for. If you were to design a model that could assist the court in making decisions, then that model should also incorporate this dictum. The primary goal of your model would be to ensure that no innocent be predicted as guilty. Similarly there are many business scenarios where the end goal is not accuracy. For such cases we need metrics like precision & recall and confusion matrix forms the basis of calculating these metrics.

Further, in case of imbalanced datasets accuracy value can be sometimes misleading. Let me illustrate my point with an example, let’s say you have to build a model that has to predict if a person is Covid positive or not based on a given set of traits. If the training data is unbalanced with overwhelming number of healthy people and only a few Covid cases, then your model would incorporate this bias. If the the test data you use has 1000 rows in which there are 950 healthy(Covid negative) cases and 50 Covid positive cases. Now, if your model predicts 990 healthy cases (Covid negative) and 10 Covid positive cases, then your model horribly fails in its objective.

What you need to know if of the corona positive cases, how many were actually predicted positive and how many were wrongly predicted as corona positive. Similarly you would want to get the True Negative and False Negative values for the healthy cases.

For capturing these values we need Confusion Matrix based metrics like precision, recall, F1 Score etc.

by : Monis Khan