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Explain with example confusion matrix, accuracy and precision .

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Confusion Matrix:

Consider a binary classification problem where we aim to predict whether an email is spam or not spam. The confusion matrix for this scenario is structured as follows:

 

                     | Predicted Not Spam | Predicted Spam |
---------------------|---------------------|-----------------|
Actual Not Spam      |        TN           |        FP       |
---------------------|---------------------|-----------------|
Actual Spam          |        FN           |        TP       |

Here:

  • TN (True Negative): Emails correctly predicted as not spam.
  • FP (False Positive): Emails incorrectly predicted as spam (Type I error).
  • FN (False Negative): Emails incorrectly predicted as not spam (Type II error).
  • TP (True Positive): Emails correctly predicted as spam.

Accuracy:

Accuracy measures the overall correctness of the model, providing the ratio of correctly predicted instances to the total instances.

Precision:

Precision gauges the accuracy of positive predictions, answering the question: Of the instances predicted as positive, how many are truly positive?

Recall (Sensitivity or True Positive Rate):

Recall assesses the model’s ability to capture all positive instances, answering: Of all actual positive instances, how many were predicted correctly?

F1 Score:

The F1 score, being the harmonic mean of precision and recall, provides a balanced measure between the two metrics. These evaluation metrics collectively offer a comprehensive assessment of a model’s performance, crucial in scenarios with imbalanced classes.

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