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    Confusion Matrix

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    Categories: Data Science

    A confusion matrix is a technique that defines the performance of a classification algorithm on a set of test data for which the true values are known. The matrix uses the True Positive(TP), True Negative (TN), False Positive(FP) and False Negative(FN) to evaluate the performance.

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