Models - Machine Learning (Machine Gnostics)

Welcome to Machine Gnostics Machine Learning Models

Machine Gnostics provides a growing suite of machine learning models for transparent, robust, and diagnostic predictive analytics. Our goal is to deliver interpretable, assumption-free machine learning solutions that combine classic algorithms with gnostic diagnostics. Whether you are working on classification, regression, clustering, or other tasks, Machine Gnostics models help you understand both predictions and underlying data structure.

NOTE

We are actively developing additional machine learning models in all categories—including supervised, unsupervised, and more. Stay tuned for updates as new tools and documentation become available.

We are open to collaboration and new ideas. If you’re interested in contributing, sharing feedback, or exploring partnerships, feel free to connect with us—your insights and creativity are always welcome!

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Key Machine Learning Model Categories

• Classification Models

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  • Logistic Regression
  • Multi Class Classification

• Regression Models

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  • Linear Regression
  • Polynomial Regression

• Clustering Models

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  • KMeans Clustering
  • Estimating Local Clustering

• Forecasting Models

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  • Auto Regressor
  • ARIMA
  • SARIMA

• Tree Models Enhanced Scikit-learn

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  • Decision Tree Regressor
  • Decision Tree Classifier
  • Random Forest Regressor
  • Random Forest Classifier

• Tree Models Enhanced XGBoost

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  • Gnostic Boosting Regressor
  • Gnostic Boosting Classifier

• Model Util

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  • Cross Validation
  • Train Test Splits
  • Gnostic Metrics

• Deployment

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  • Mlflow Integration

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Why Use Machine Gnostics Machine Learning Models?

• Transparent: Built-in diagnostics and error analysis for every model.
• Assumption-Free: No strict requirements on data distribution or linearity.
• Robust: Handles outliers, non-normality, and real-world data challenges.
• Extensible: Integrates seamlessly with Python data science and ML workflows.
• Expanding: New models and features are continuously being added.

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Getting Started

Explore the documentation for each model to learn about their features, usage patterns, and example workflows.

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

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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 machine learning pipeline for robust, diagnostic predictive analytics.
• Check back regularly for new models and updates as our development continues.

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"In Machine Gnostics, every model is a step toward deeper understanding—of your data, your process, and your discoveries."