Week 9 : Classification

Confusion Matrix

Interpretation of indicator: (Here)

• Proportion of times the classifier is correct

Precision (吾係變係)

• To determine the best model when the costs of False Positive is high.
• For instance, email spam detection.
• In email spam detection, a false positive means that an email that is non-spam (actual negative) has been identified as spam (predicted spam).
• The email user might lose important emails if the precision is not high for the spam detection model.

Recall (係變吾係) aka Sensitivity

% of predicting a YES as YES = Low values imply that the model does not predict the positive class well.

• To determine the best model when the cost associated with False Negative is high.

• Calculates how many of the Actual Positives our model capture through labeling it as Positive (True Positive).
• For instance of fraud detection,
• If a fraudulent transaction (Actual Positive) is predicted as non-fraudulent (Predicted Negative), the consequence can be very bad for the bank.
• For instance of sick patient detection,
• Similarly, in sick patient detection, if a sick patient (Actual Positive) goes through the test and predicted as not sick (Predicted Negative).
• The cost associated with False Negative will be extremely high if the sickness is contagious.

Specificity

% of predicting a NO as NO = Low values imply that the model does not predict the negative class well.