Models - Data Analysis (Machine Gnostics)¶

Welcome to Machine Gnostics Data Analysis Models¶

Machine Gnostics provides a unified framework for robust, assumption-free data analysis using advanced statistical and gnostic theory principles. The "Models" section is your entry point to understanding the core analytical tools available in this library.

What Are Machine Gnostics Models?¶

Machine Gnostics models are specialized classes and algorithms designed to analyze, interpret, and diagnose data distributions. They go beyond traditional statistics by focusing on universal properties, diagnostic features, and gnostic error measures, making them suitable for a wide range of scientific, engineering, and machine learning applications.

Key Data Analysis Model Categories¶

Gnostic Distribution Distributions (GDF)¶

EGDF (Estimating Global Distribution Function)
ELDF (Estimating Local Distribution Function)
QGDF (Quantifying Global Distribution Function)
QLDF (Quantifying Local Distribution Function)

These models provide flexible, non-parametric representations of data distributions, supporting both empirical and quantile-based analysis. For more information, see GDF documentation.

Gnostic Data Tests¶

Homogeneity Test

Functions for assessing data uniformity, analyzing distribution consistency and structural regularity.
Scedasticity Test

Functions for assessing data variance, volatility, and dispersion characteristics across the dataset.
Membership Test

Algorithms for quantifying membership relevance and diagnostic scores for individual data points.

Data Analysis¶

Cluster Analysis

Tool for identifying natural groupings and structure in data.
Interval Analysis

Tool for estimating confidence intervals, bounds, and diagnostic regions with robust handling of outliers.
Data Cluster

Advanced boundary detection for identifying main clusters from probability density functions.
Data Interval

Robust interval estimation engine for adaptive scanning and extracting meaningful data ranges.

Each page provides a detailed overview, key features, parameters, example usage, and references.

Why Use Machine Gnostics Models?¶

Assumption-Free: No reliance on normality, linearity, or parametric forms.
Universal: Applicable to any data type or domain.
Diagnostic: Built-in error estimation, entropy measures, and robust statistics.
Extensible: Easily integrates with existing Python data science workflows.

Next Steps¶

Browse individual model pages for in-depth documentation and code examples.
Try out example notebooks in the examples folder for hands-on learning.
Integrate models into your own analysis pipeline for robust, diagnostic data science.