F1 Score Practice Problem

This data science coding problem helps you practice Evaluation Metrics for Classification, f1 score, and implementation skills. Read the problem statement, write your solution, and strengthen your understanding of Evaluation Metrics for Classification.

Problem ID: 147
Problem key: 147-f1-score
URL: https://datacrack.app/solve/147-f1-score
Difficulty: easy
Topic: Evaluation Metrics for Classification
Module: Introduction to Machine Learning

Problem Statement

## 🧩 F1 Score

### 🎯 Goal
Compute the **F1 Score** — a metric that balances both precision and recall into a single value.

---

### 📥 Input / 📤 Output

**Input**
- `y_true` (`list[int]`): true labels (0 or 1)
- `y_pred` (`list[int]`): predicted labels (0 or 1)

**Assumption:**  
- `y_true` and `y_pred` have the same non-zero length.
- Labels are binary (0 or 1).

**Output**
- `float`: F1 score

---

### 💻 Task Description

The F1 Score is the **harmonic mean** of precision and recall. It provides a better measure of a model's performance on imbalanced datasets than accuracy.

It is defined as:

$$
F_1 = 2 \cdot \frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}}
$$

Steps to compute:
1. Calculate **TP**, **FP**, and **FN**.
2. Compute **Precision** = $TP / (TP + FP)$.
3. Compute **Recall** = $TP / (TP + FN)$.
4. Compute **F1 Score** using the formula above.

⚠️ **Edge Case:** If (Precision + Recall) is 0, the F1 Score should be `0.0`.

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### 🧩 Starter Code

```python
def f1_score(y_true, y_pred):
    """
    Returns the F1 Score.
    """
    pass
```

---

### 💡 Example
```python
y_true = [1, 0, 1, 1, 0]
y_pred = [1, 1, 1, 0, 0]

f1_score(y_true, y_pred)
```

**Expected Output**
```
0.7272727273
```

---

Starter Code

def f1_score(y_true, y_pred):
    """
    Returns the F1 Score.
    """
    pass

F1 Score Practice Problem

Problem Statement

Starter Code

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