EP01_02 — 📊 Predictive Performance — ML Metrics in CV

1.18.5 EP01_02 📊 Predictive Performance — ML Metrics in CV

In this activity, you will dive into the world of Machine Learning. Your goal is to evaluate the performance of a binary classifier by calculating metrics from a Confusion Matrix.

1.18.5.1 🧠 Why does the right metric matter?

Imagine a R$ 1.00 coin detector. The impact of the error defines the priority metric:

Metric	Practical Example	Importance in IP/CV
Accuracy	Grain Counting	Useful when classes are balanced (e.g., half of the grains defective, half healthy).
Precision	Security/Biometrics	Crucial to avoid False Positives (e.g., not allowing an impostor to access a system due to recognition errors).
Sensitivity	Health (Tumors)	Crucial to avoid False Negatives (e.g., not letting a tumor go unnoticed in an X-ray examination).
F1-score	Banknotes	Ideal for a balance between not rejecting genuine notes and not accepting counterfeit ones.

1.18.5.2 📊 The Confusion Matrix

	Predicted Positive	Predicted Negative
Actual Positive	TP (True Positive)	FN (False Negative)
Actual Negative	FP (False Positive)	TN (True Negative)

Task:

Read 4 integer values in the order: TP, FN, FP, TN.
Calculate the metrics using the formulas:

\[\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}\]

\[\text{Precision} = \frac{TP}{TP + FP}\]

\[\text{Sensitivity (Recall)} = \frac{TP}{TP + FN}\]

\[\text{F1-score} = \frac{2 \times \text{Precision} \times \text{Sensitivity}}{\text{Precision} + \text{Sensitivity}}\]

Print the results formatted with two decimal places, one per line.

📌 Important:

Use floating-point division to avoid truncated results.
The output order must be: Accuracy, Precision, Sensitivity, and F1-score.
See the interactive simulation for this EP at Figure 1.12.

1.18.5.3 📌 Example Execution

Input	Output	Observation
`40`	`0.75`	Accuracy
`10`	`0.73`	Precision
`15`	`0.80`	Sensitivity
`35`	`0.76`	F1-score

(Note: TP=40, FN=10, FP=15, TN=35. Total cases = 100)

🎮 Simulator EP01_02: Predictive Performance — ML Metrics Confusion Matrix & Metrics

Select a predefined scenario or adjust the sliders to observe the real-time impact on the confusion matrix and evaluation metrics.

INPUTS (MATRIX PARAMETERS)

TP 40

FN 10

FP 15

TN 35

DISCRETE CONFUSION MATRIX

Pred +
Pred −
Actual +
TP
40
FN
10
Actual −
FP
15
TN
35

REAL-TIME CALCULATED METRICS

Accuracy 0.75

(TP + TN) / Total

Precision 0.73

TP / (TP + FP)

Sensitivity (Recall) 0.80

TP / (TP + FN)

F1-Score (Harmonic Mean) 0.76

2 · (P · R) / (P + R)

Total Samples: 100

Figure 1.12: EP01_02 Simulator: Predictive Performance — ML Metrics

# your solution

TestSuite("EP01_02.py").run()

✔️ EP01_02.cases already exists in casos/
📋 5 case(s) loaded from casos/EP01_02.cases
💥 File EP01_02.py not found.