Subject: Special Topics $-$ Machine Learning

Assignment #$1$: Understanding Performance Metrics: Calculating Accuracy, Precision, Recall, and F$1-$score.

Objective:

The aim of this assignment is to provide students with hands$-$on experience in calculating key performance metrics: Accuracy, Precision, Recall, and F$1-$score$-$from a confusion matrix. Additionally, students will utilize Excel functions to automate the calculation process, enhancing their proficiency in both manual calculation and utilizing digital tools for analysis.

Assignment Tips:

Ensure a solid understanding of confusion matrices and the concepts of true positives, true negatives, false positives, and false negatives.

Familiarize yourself with the formulas for Accuracy, Precision, Recall, and F$1-$score and how they are derived from the confusion matrix.

Practice using Excel functions such as SUM, and AVG, to efficiently calculate the metrics.

Double$-$check your calculations and formula implementations to avoid errors.

Pay attention to the interpretation of each metric and understand their significance in evaluating classification model performance.

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Task $1$: Understanding Confusion Matrices

Define a confusion matrix and explain its components.

Provide a hypothetical scenario where a confusion matrix would be utilized $($e$.$g$.,$ diagnosing medical conditions$).$

Task $2$: Manual Calculation of Metrics

Given the following confusion matrix values, manually calculate the Accuracy, Precision, Recall, and F$1-$score:

$>$ True Positives $(TP)=120$

$>$ True Negatives $(TN)=80$

$>$ False Positives $(FP)=20$

$>$ False Negatives $(FN)=10$