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Models - Data Analysis (Machine Gnostics)

Welcome to Machine Gnostics 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

  • GDF Distributions
  • EGDF (Empirical Gnostics Distribution Function)
  • ELDF (Empirical Likelihood Distribution Function)
  • QGDF (Quantile Gnostics Distribution Function)

  • QLDF (Quantile Likelihood Distribution Function)
    These models provide flexible, non-parametric representations of data distributions, supporting both empirical and quantile-based analysis.

  • Cluster Analysis
    Tools for identifying natural groupings and structure in data, supporting unsupervised learning and exploratory analysis.

  • Interval Analysis
    Methods for estimating confidence intervals, bounds, and diagnostic regions in data, with robust handling of outliers and non-normality.

  • Homogeneity & Scedasticity
    Functions for assessing data uniformity, variance, and diagnostic properties, enabling deeper understanding of data quality and structure.

  • Membership & Data Diagnostics
    Algorithms for quantifying membership, relevance, and diagnostic scores for individual data points.

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.

Getting Started

Explore the documentation for each model to learn about their features, usage patterns, and example workflows.
- EGDF
- ELDF
- QGDF
- QLDF
- Cluster Analysis
- Interval Analysis
- Homogeneity
- Scedasticity
- Membership
- Data Cluster
- Data Interval
- Z0 Estimator

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

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.

If you want this saved as models.md in your documentation folder, let me know!