Accuracy Precision Recall example 39

What this example teaches

Accuracy Precision Recall

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 Accuracy Precision Recall 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 accuracy precision recall without surrounding noise, so you can see the idea clearly.

Where it is used in real projects

Accuracy Precision Recall 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 Accuracy Precision Recall example and run it again.

Advanced variation

Combine Accuracy Precision Recall with validation, error handling or reusable structure.