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A confusion matrix is a type of table construct that plays a specific role in machine learning and related engineering. It helps to show the prediction and recall in a system where the values of the test data are known.
Typically, a confusion matrix treats a binary process of classification. The resulting table is composed of two rows and two columns, filled with four values – true positives, false positives, true negatives and false negatives.
In the confusion matrix, a true positive exists where observation is positive with a positive prediction. A false positive exists where observation is negative, with a positive prediction. A true negative exists where observation is negative with negative prediction, and a false negative indicates a positive observation with a negative prediction.
Assorted equations then show how to calculate accuracy and precision for a given project. Many confusion matrix projects will utilize tools like Python SciKit or Numpy or others.
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