Classification example 9
A focused Machine Learning example for classification with output and explanation.
Classification example 9
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Input
Terminal
SuccessReady.
Run code to see output here.
What this example teaches
Classification
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 Classification example: actual = [1, 0, 1, 1, 0, 0].
- Line 2 adds one required part of the working pattern: predicted = [1, 0, 0, 1, 1, 0].
- Line 3 adds one required part of the working pattern: true_positive = sum(a == 1 and p == 1 for a, p in zip(actual, predicted)).
- Line 4 adds one required part of the working pattern: false_positive = sum(a == 0 and p == 1 for a, p in zip(actual, predicted)).
- Line 5 adds one required part of the working pattern: false_negative = sum(a == 1 and p == 0 for a, p in zip(actual, predicted)).
- Line 6 adds one required part of the working pattern: precision = true_positive / (true_positive + false_positive).
Why this example is useful
This example is useful because it isolates classification without surrounding noise, so you can see the idea clearly.
Where it is used in real projects
Classification 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 Classification example and run it again.
Advanced variation
Combine Classification with validation, error handling or reusable structure.