Tech Tips for Life: Confusion Matrix: The Key to Precision in Classification Analysis

Today, I am pleased to welcome Khun Mathee Prasertkijaphan from SCB TechX’s Data Analytics team, who will guide us through evaluating the performance of Machine Learning models using the Confusion Matrix. This matrix works with known data outcomes, comparing actual values to predicted ones from the model.

Here are the key components of the matrix that you need to know first (as shown in Table 1):

1. True Negative (TN): When the model correctly predicts a negative outcome, like predicting that a man is not pregnant (Negative), and the actual outcome is that he is not pregnant (True).
2. False Positive (FP) or Type 1 Error: When the model incorrectly predicts a positive outcome, like predicting that a man is pregnant (Positive) when he is actually not (False).
3. False Negative (FN) or Type 2 Error: When the model incorrectly predicts a negative outcome, like predicting that a woman is not pregnant (Negative) when she actually is (False).
4. True Positive (TP): When the model correctly predicts a positive outcome, like predicting that a woman is pregnant (Positive) and she actually is (True).

Table 1 presents four methods for evaluating model performance, and the right choice depends on your specific needs:

1. Accuracy: Ideal for general performance evaluation without focusing on the type of error.
2. Precision: Important when prediction accuracy is crucial, such as identifying important emails as spam.
3. Recall: Best used when prediction errors could have serious consequences, commonly used in medical predictions (Save Life) where a high Recall is desired, such as predicting HIV status.
4. F1-Score: Balances Precision and Recall, suitable for analyses requiring both accuracy and coverage, such as stock market analysis.

The confusion matrix is a valuable tool for evaluating the performance of a Machine Learning model by comparing predicted outcomes with actual results, allowing us to identify accuracy and types of errors for further model enhancement.

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