Loss Functions example 33

What this example teaches

Loss Functions

Output

The experiment prepares features, labels, metrics and validation rows, trains or scores a small model pattern, and prints a metric you can compare.

Line-by-line explanation

• Line 1 sets up the Loss Functions example: hours = [1, 2, 3, 4].
• Line 2 adds one required part of the working pattern: scores = [42, 51, 63, 72].
• Line 3 adds one required part of the working pattern: slope = 10.
• Line 4 adds one required part of the working pattern: intercept = 32.
• Line 5 adds one required part of the working pattern: predictions = [slope * hour + intercept for hour in hours].
• Line 6 adds one required part of the working pattern: mae = sum(abs(actual - pred) for actual, pred in zip(scores, predictions)) / len(scores).

Why this example is useful

This example is useful because it isolates loss functions without surrounding noise, so you can see the idea clearly.

Where it is used in real projects

Loss Functions appears in real Machine Learning work when a feature needs a clear pattern that can be reviewed and changed safely.

Beginner variation

Change one label, value or condition in the Loss Functions example and run it again.

Advanced variation

Combine Loss Functions with validation, error handling or reusable structure.