Performance Evaluation Parameters of a Machine Learning Model

Performance Evaluation Parameters of a Machine Learning Model

This gives a good idea of how our model performs it not only tells us how many classifications are done are correctly and how many of them are classified incorrectly but it tells more.

Understanding Various methods used to measure the performance of a Machine Learning Model.It is very Important to understand thoose to further improve on the aspect that is best suited for our Problem Statement.

So let us explore………

Confusion Matrix

As the name suggests the Confusion Matrix is really confusing but let me break it down for you and explain it.Confusion Matrix is one of the most popular and most used Evaluation Parameters used in Classification Problems. This gives a good idea of how our model performs it not only tells us how many classifications are done are correctly and how many of them are classified incorrectly but it tells more.

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So here we have 2 rows positive and negative corresponding to predicted values and 2 columns positive and negative corresponding to actual values.

So we get a 2*2 matrix which has four positions to be filled let us describe all four of them.

1] True Positive-These are the samples which are correctly classified. They were True or 1 and they were predicted as 1

Eg — Telling a pregnant women that she is pregnant

2] True Negative-These are the samples which are correctly classified. They were False or 0 and they were predicted as 0

Eg — Telling a man that he is not pregnant

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