make_linnerud_check_data: Linnerud Multi-output Regression Dataset¶

The make_linnerud_check_data function loads or generates a Linnerud-like multi-output regression dataset. This dataset consists of 3 exercise data variables and 3 physiological variables collected from 20 middle-aged men in a fitness club. It is a classic dataset for multi-output regression tasks.

Overview¶

This utility provides access to the Linnerud dataset:

Features (\(X\)): Three exercise variables: Chins, Situps, and Jumps.
Targets (\(Y\)): Three physiological variables: Weight, Waist, and Pulse.
Purpose: Ideal for testing models that predict multiple targets simultaneously (multi-output regression).
Source: Wraps Scikit-learn's load_linnerud if available; otherwise falls back to a similarly shaped synthetic generator.

Parameters¶

Parameter	Type	Description	Default
`return_names`	bool	If `True`, returns the list of column names for \(X\) and \(Y\).	`True`

Returns¶

Return	Type	Description
`X`	numpy.ndarray	Exercise features of shape `(20, 3)`. Columns correspond to `Chins`, `Situps`, `Jumps`.
`Y`	numpy.ndarray	Physiological targets of shape `(20, 3)`. Columns correspond to `Weight`, `Waist`, `Pulse`.
`X_names`	list[str]	Names of the feature columns `['Chins', 'Situps', 'Jumps']`. Returned only if `return_names=True`.
`Y_names`	list[str]	Names of the target columns `['Weight', 'Waist', 'Pulse']`. Returned only if `return_names=True`.

Example Usage¶

from machinegnostics.data import make_linnerud_check_data

# Load data with column names
X, Y, X_names, Y_names = make_linnerud_check_data(return_names=True)

print(f"Features: {X_names}")
# Output: ['Chins', 'Situps', 'Jumps']

print(f"Targets: {Y_names}")
# Output: ['Weight', 'Waist', 'Pulse']

print(f"X Shape: {X.shape}, Y Shape: {Y.shape}")
# Output: X Shape: (20, 3), Y Shape: (20, 3)