spec: add learning-curve-basic specification (#2280)

## New Specification: `learning-curve-basic`

Related to #2275

---

### specification.md

# learning-curve-basic: Model Learning Curve

## Description

A learning curve visualizes model performance (training and validation
scores) as a function of training set size. It is essential for
diagnosing bias vs variance tradeoffs, determining whether collecting
more data would improve model performance, and guiding model selection
decisions. The plot typically shows two lines with shaded confidence
bands representing variability across cross-validation folds.

## Applications

- Diagnosing underfitting (high bias) when both training and validation
scores are low
- Diagnosing overfitting (high variance) when training score is high but
validation score is low with a large gap
- Determining if collecting more training data would improve model
performance
- Comparing learning characteristics across different model
architectures

## Data

- `train_sizes` (numeric) - Array of training set sizes used for
evaluation
- `train_scores` (numeric) - Training scores at each sample size (2D:
folds × sizes)
- `validation_scores` (numeric) - Validation scores at each sample size
(2D: folds × sizes)
- Size: 5-20 different training set sizes, typically with 5-10
cross-validation folds
- Example: Scikit-learn's `learning_curve` function output

## Notes

- Use shaded regions to show confidence bands (e.g., ±1 standard
deviation across folds)
- Clearly label the y-axis with the metric being evaluated (accuracy,
F1, MSE, etc.)
- Include a legend distinguishing training from validation curves
- X-axis should show actual sample sizes or percentages of total
training data
- Consider using distinct colors (e.g., blue for training, orange for
validation) for clarity

---

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---
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plots/learning-curve-basic/specification.md

@@ -0,0 +1,28 @@
+# learning-curve-basic: Model Learning Curve
+
+## Description
+
+A learning curve visualizes model performance (training and validation scores) as a function of training set size. It is essential for diagnosing bias vs variance tradeoffs, determining whether collecting more data would improve model performance, and guiding model selection decisions. The plot typically shows two lines with shaded confidence bands representing variability across cross-validation folds.
+
+## Applications
+
+- Diagnosing underfitting (high bias) when both training and validation scores are low
+- Diagnosing overfitting (high variance) when training score is high but validation score is low with a large gap
+- Determining if collecting more training data would improve model performance
+- Comparing learning characteristics across different model architectures
+
+## Data
+
+- `train_sizes` (numeric) - Array of training set sizes used for evaluation
+- `train_scores` (numeric) - Training scores at each sample size (2D: folds × sizes)
+- `validation_scores` (numeric) - Validation scores at each sample size (2D: folds × sizes)
+- Size: 5-20 different training set sizes, typically with 5-10 cross-validation folds
+- Example: Scikit-learn's `learning_curve` function output
+
+## Notes
+
+- Use shaded regions to show confidence bands (e.g., ±1 standard deviation across folds)
+- Clearly label the y-axis with the metric being evaluated (accuracy, F1, MSE, etc.)
+- Include a legend distinguishing training from validation curves
+- X-axis should show actual sample sizes or percentages of total training data
+- Consider using distinct colors (e.g., blue for training, orange for validation) for clarity

plots/learning-curve-basic/specification.yaml

@@ -0,0 +1,30 @@
+# Specification-level metadata for learning-curve-basic
+# Auto-synced to PostgreSQL on push to main
+
+spec_id: learning-curve-basic
+title: Model Learning Curve
+
+# Specification tracking
+created: 2025-12-26T17:28:31Z
+updated: 2025-12-26T17:28:31Z
+issue: 2275
+suggested: MarkusNeusinger
+
+# Classification tags (applies to all library implementations)
+# See docs/concepts/tagging-system.md for detailed guidelines
+tags:
+  plot_type:
+    - line
+    - learning-curve
+  data_type:
+    - numeric
+    - continuous
+  domain:
+    - machine-learning
+    - statistics
+    - data-science
+  features:
+    - basic
+    - confidence-band
+    - comparison
+    - diagnostic