F1 Score example 40
A focused Machine Learning example for f1 score with output and explanation.
F1 Score example 40
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Input
Terminal
SuccessReady.
Run code to see output here.
What this example teaches
F1 Score
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 F1 Score example: dataset = [.
- Line 2 adds one required part of the working pattern: {"feature": 1.2, "label": "low"},.
- Line 3 adds one required part of the working pattern: {"feature": 3.8, "label": "high"},.
- Line 4 adds one required part of the working pattern: {"feature": 2.4, "label": "medium"},.
- Line 5 adds one required part of the working pattern: ].
- Line 6 adds one required part of the working pattern: features = [row["feature"] for row in dataset].
Why this example is useful
This example is useful because it isolates f1 score without surrounding noise, so you can see the idea clearly.
Where it is used in real projects
F1 Score 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 F1 Score example and run it again.
Advanced variation
Combine F1 Score with validation, error handling or reusable structure.