make_classification_check_data: Synthetic Classification Dataset

The make_classification_check_data function generates a synthetic dataset for validating classification models within the Machine Gnostics framework. This function creates simple blob-based clusters using Gaussian distributions, serving as a reliable "hello world" test for classification algorithms.

---

Overview

This utility simplifies the creation of classification datasets by generating clusters of points centered around randomly positioned centroids.

• Method: Gaussian blobs with configurable separability.
• Purpose: Unit testing models, verifying pipeline integrity, and demonstrating basic classification capabilities.
• Customization: Easily adjust the number of samples, features, classes, and task difficulty (separability).

---

Parameters

Parameter	Type	Description	Default
n_samples	int	Total number of data points to generate.	30
n_features	int	Number of input features (dimensions) per sample.	2
n_classes	int	Number of distinct classes (labels).	2
separability	float	Distance multiplier for class centers. Higher values = easier separation.	2.0
seed	int	Random seed for reproducibility.	42

---

Returns

Return	Type	Description
X	numpy.ndarray	Input feature array of shape (n_samples, n_features).
y	numpy.ndarray	Target label array of shape (n_samples,).

---

Example Usage

from machinegnostics.datasets import make_classification_check_data
import numpy as np

# Generate a 3-class dataset with 50 samples
X, y = make_classification_check_data(n_samples=50, n_classes=3)

print(f"X shape: {X.shape}")
print(f"Unique classes: {np.unique(y)}")
# Output:
# X shape: (50, 2)
# Unique classes: [0 1 2]

---

Author: Nirmal Parmar