What are the Classification Metrics in AI?
If you are not sure of justifying whether or not, it can be regarded as something similar to the fact that you wish to get something, but you are not aware of what it is. In this post, we aim covering multiple classification metrics.
Classification Metrics in Artificial Intelligence
Introduction to Confusion Matrix
Confusion Matrix is required to be mentioned while introducing classification metrics. TP True Positive, TN True Negative, FP False Positive, and FN False Negative serves to be some of the basic elements.
True positive: The assumed result turns out to be positive, while the same has been labeled as positive.
True Negative: The assumed result turns out to be negative, while the same has been labeled as negative. It also goes by the name as Type II Error.
False Positive: The assumed result turns out to be positive, while the same has been labeled as negative. It is also known to go by the name as Type I Error.
True Negative: The assumed result turns out to be negative, while the same has been labeled as positive.
On the basis of the given four elements, you can extend the same to multiple calculations like specificity, recall, and precision.
Easy to Understand or Straightforward (Accuracy)
Accuracy turns out to be one of the easiest measurements. It is simply known to analyze the total number of correct classifications out of the total count. When the classification problem happens to be balance, you can make use of the accuracy metric to serve as one of the major measurement metrics.
The Choice Dilemma (Recall & Precision)
While accuracy serves to be straightforward as well as easy to interpret, it might not always serve to be a great metric. For instance, you would want to classify in some rare cases, like whether an individual is a CEO or a billionaire. In rare cases, it is increasingly easier to achieve 99.9 percent accuracy.
Balance Dilemma (F1)
F1 is introduced in case the classification problem request for high recall and precision while accuracy is not regarded as good for measurement. From the given formula, you can observe that F1 is known to include both recall and precision result:
F1 = (2X Precision X Recall) / (Precision + Recall)
F1 can be utilized if you would want to balance recall and precision while the overall distribution is extremely uneven.
ROC (Receiver Operator Characteristic)
It is yet another metric that is utilized for representing the classification result. Unlike recall and precision, ROC includes false which implies the number of record that classifies to be negative and it is deemed to be correct. The metrics has been designed for the binary classifier. If the business problem serves to be a multi-class problem, you can look forward to converting the same to multiple problems of binary classification during the measurement.