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Machine Gnostics – Example Gallery (Google Colab)

You can try Machine Gnostics instantly with Google Colab notebooks! Click any link below, then select "Open with Google Colab" to launch the notebook in your browser—no installation required.

Explore practical examples and Jupyter notebooks demonstrating the use of Machine Gnostics for data analysis and machine learning. Each example includes code, explanations, and links to downloadable notebooks.

Data Analysis Examples

  1. Gnostics Measures and Distribution Functions
    Learn how to compute and interpret Gnostic measures and fit Gnostic Distribution Functions (GDFs) to your data.

  2. Gnostics Tests for Data Samples
    Apply Gnostic homogeneity, scedasticity, and membership tests to assess the structure and quality of your data samples.

  3. Gnostics Cluster Analysis
    Use Gnostic methods to identify main clusters, estimate cluster bounds, and visualize cluster structure in complex datasets.

  4. Gnostics Interval Analysis
    Perform robust interval estimation (tolerance, typical, and cluster intervals) using GDFs for scientific and engineering data.

  5. Gnostics Uncertainty Analysis – Real Life Example
    Analyze uncertainty in real-world data using Gnostic diagnostics, interval analysis, and visualization tools.

Machine Learning Examples

  1. Small Data Regression – Linear Regression
    Fit and evaluate a linear regression model on a small dataset, demonstrating robust fitting and prediction with Machine Gnostics.

  2. Wine Quality: Multidimensional Linear Regression
    Apply linear regression to the wine quality dataset with multiple features, showing how to handle multivariate regression tasks.

  3. Small Data Polynomial Regression
    Explore polynomial regression on a small dataset, including fitting, prediction, and the impact of nonlinear relationships.

  4. Wine Quality: Multidimensional Polynomial Regression
    Perform advanced polynomial regression on the wine quality dataset, highlighting feature expansion and robust modeling.

  5. Basic Binary Logistic Regression
    Train and evaluate a robust logistic regression classifier on synthetic moon data, including probability estimation and decision boundaries.

  6. Polynomial Regression with MLflow Integration
    Integrate polynomial regression with MLflow for experiment tracking, reproducibility, and deployment in a real-world workflow.

Access the Notebooks

You can download or view all Jupyter notebooks from the examples directory on Google Drive.

More Information

For more details on the Machine Gnostics Foundation, visit the Learn section.

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Try running these notebooks in the Google Colab cloud environment to explore Machine Gnostics by yourself!