# Accuracy

This is the second blog of series -“Evaluation Metric”. Hope you have idea about Confusion Matrix and terms involved with it-: TP, TN etc. If you want to look at it just go through this tutorial: http://blog.robofied.com/model-evaluation-metrics/

Let’s consider this confusion matrix :

By its name one can easily guess that Accuracy is used to measure how accurate our model i.e, how many positives are predicted positive and how many negatives are predicted negative.

## Accuracy = (TP+TN)/(TP+TN+FP+FN)

From the above formula, we deduce-:

In numerator there are correct predictions made by model and in denominator, there is involvement of all the terms whether it is wrong or right.

## Advantages of using Accuracy

- It is easy to use.
- It gives general overview of Accuracy.

## Disadvantages of using Accuracy

- It gives bad results if data set is unbalanced i.e, equal no. of observations belong to each class.
- It is not judgemental i.e, we cannot say higher accuracy means good model and vice-versa -: Sometimes it may be desirable to select a model with a lower accuracy because it has a greater predictive power on the problem.

Hope you enjoyed reading this blog.

This is for accuracy evaluation metric.