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A confusion matrix is a performance measurement technique for Machine learning classification. 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. The term confusion matrix itself is very simple, but its related terminology can be a little confusing. Here, some simple explanation is given for this technique.
In this tutorial, you will learn,
The confusion matrix visualizes the accuracy of a classifier by comparing the actual and predicted classes. The binary confusion matrix is composed of squares:
You can compute the accuracy test from the confusion matrix:
Confusion Matrix is a useful machine learning method which allows you to measure Recall, Precision, Accuracy, and AUC-ROC curve. Below given is an example to know the terms True Positive, True Negative, False Negative, and True Negative.
True Positive:
You projected positive and its turn out to be true. For example, you had predicted that France would win the world cup, and it won.
True Negative:
When you predicted negative, and it's true. You had predicted that England would not win and it lost.
False Positive:
Your prediction is positive, and it is false.
You had predicted that England would win, but it lost.
False Negative:
Your prediction is negative, and result it is also false.
You had predicted that France would not win, but it won.
You should remember that we describe predicted values as either True or False or Positive and Negative.
Here, is step by step process for calculating a confusion Matrix in data mining
After that, these numbers are organized in the below-given methods:
The maximum score is 1 when the classifier perfectly classifies all the positive values. Precision alone is not very helpful because it ignores the negative class. The metric is usually paired with Recall metric. Recall is also called sensitivity or true positive rate.
Here are pros/benefits of using a confusion matrix.
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