For binary classification problems, a confusion matrix is a summary table showing the number of accurate and wrong guesses (or actual and anticipated values) generated by a classifier (or classification model).

In simple words,

- The ways in which a classification model becomes confused when making predictions are shown in a confusion matrix.
- Large values across the diagonal and lower values off the diagonal describe a good matrix (model).
- Measuring a confusion matrix gives us a better idea of whether our classification model is right and what kinds of mistakes it is causing.

- This is a table with four different sets of predicted and actual values.
- The table compares predicted values in Positive and Negative and actual values as True and False.
- These four variables form the base for creating a confusion matrix.
**True Positive(TP) -**Number of correctly labelled positive samples**False Positive(FP) -**Number of negative samples incorrectly labelled as positive**True Negative(TN) -**Number of correctly labelled negative samples**False Negative(FN) -**Number of positive samples incorrectly labelled as negative

**Making definition (An example of cricket) :**

*The batsman is NOT OUT, a positive class or logic 1.**The batsman is OUT, a negative class or logic 0.*

Now in terms with the 2x2 confusion matrix;

**True positive:**An umpire gives a batsman**NOT OUT**when he is actually**NOT OUT**.**True Negative:**When an umpire gives a batsman**OUT**when he is actually**OUT**.**False Positive (Type 1 error):**This is the condition a batman is given**NOT OUT**when he is actually**OUT**.**False Negative (Type 2 error):**When an umpire gives a batman**OUT**when he is actually**NOT OUT**.

- It provides information on the sorts of errors produced by the classifier as well as the errors themselves.
- This feature assists in prevailing over the limitations of deploying classification accuracy alone.
- It is used in situations where there is a significant imbalance in the classification issue, with one class dominating the others.
- Recall, Precision, Specificity, Accuracy, and the AUC-ROC Curve can all be calculated using the confusion matrix. (We will covering those topics in the next articles).

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