Don’t get confused with the word confusion matrix, there is nothing to be so confused about it. In simple terms, the confusion matrix is used to identify the best performing algorithms within several competing algorithms.
Please refer to the figure to have a view of the confusion matrix, it consists of true positive, false positive, false negative and true negative. We have already discussed about true positive, false positive, false negative and true negative in our earlier sections.
If the number of true positive and true negative is more in the confusion matrix then the algorithm is performing well, but, if, number of false positive and false negative increases, the performance of the algorithm is not good.
The confusion matrix can have many dimension and it depends on the number of outcomes we want to predict, for example, if we want to predict for ‘number of outcomes then the confusion matrix will have n dimensions.
The diagonal elements in the confusion matrix will be those elements which are correctly classified by the machine learning algorithm.
Don't get confused about confusion matrix, it is just a matrix of identifying the best machine learning algorithm amongst competing algorithms.
If you are interested to know more about the confusion matrix, you can mail to smartsubu2020@gmail.com.
