09
Oct
What Is Confusion Matrix In Data Mining. It is a kind of table which helps you to the know the performance of the classification model on a set of test data for that the true values are known. A confusion matrix tells you how good a classification algorithm is. This indicates the quality of the current model. Actual Class Target VE.
Actual Class Target VE. As you saw in the video a confusion matrix is a very useful tool for calibrating the output of a model and examining all possible outcomes of your predictions true. It displays the distribution of the records in terms of their actual classes and their predicted classes. This indicates the quality of the current model. It is a matrix of size 22 for binary classification with actual values on one axis and predicted on another. Confusion Matrix is a performance measurement for machine learning classification.
A confusion matrix of binary classification is a two by two table formed by counting of the number of the four outcomes of a binary classifier.
It is a kind of table which helps you to the know the performance of the classification model on a set of test data for that the true values are known. It calculates the proportion of correctly classified instances. Confusion matrix shows the total number of correct and wrong predictions. And it is where the Confusion matrix comes into the limelight. It can only be determined if the true values for test data are known. Actual Class Target VE.