Machine Gnostics – Example Gallery

Explore practical examples and Jupyter notebooks demonstrating the use of Machine Gnostics for data analysis and machine learning.

Each example includes:

• Comprehensive code examples
• Theoretical background and explanations
• Immediate execution via Google Colab
• Source code access on GitHub

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Tutorials Library

Data Analysis ModelsMachine Learning ModelsIntegrations & ToolsDeep Learning

Metrics

• Machine Gnostics Basic Metrics

  Learn how to calculate and interpret core Gnostics metrics.

  Open in Colab · GitHub

Data Tests

• Data Homogeneity Test

  Assess if your data comes from a single population or distribution.

  Open in Colab · GitHub

• Homoscedasticity Test

  Test for constant variance across your dataset (homoscedasticity).

  Open in Colab · GitHub

• Data Membership Test

  Evaluate whether new data points belong to the training distribution.

  Open in Colab · GitHub

Advanced Analysis

• Gnostic Distribution Functions

  Visualize and analyze data using Gnostics Distribution Functions.

  Open in Colab · GitHub

• Marginal Cluster Analysis

  Perform clustering analysis using marginal distributions.

  Open in Colab · GitHub

• Marginal Interval Analysis

  Analyze data intervals and bounds with marginal analysis.

  Open in Colab · GitHub

• Uncertainty Interval Analysis

  Quantify and analyze uncertainty within your data intervals.

  Open in Colab · GitHub

Regression

• Linear Regression

  Standard linear regression implementation.

  Open in Colab · GitHub

• Polynomial Regression

  Regression with polynomial features.

  Open in Colab · GitHub

• Wine Quality Regression

  Real-world example: Predicting wine quality.

  Open in Colab · GitHub

• Decision Tree Regressor

  Non-linear regression using Decision Trees.

  Open in Colab · GitHub

• Random Forest Regressor

  Ensemble regression using Random Forests.

  Open in Colab · GitHub

• Boosting Regressor

  Advanced regression using Boosting techniques.

  Open in Colab · GitHub

Classification

• Logistic Regression

  Binary classification fundamentals.

  Open in Colab · GitHub

• Multiclass Classification

  Handling multiple classes in classification tasks.

  Open in Colab · GitHub

• Decision Tree Classifier

  Classification using Decision Tree algorithms.

  Open in Colab · GitHub

• Random Forest Classifier

  Robust classification with Random Forests.

  Open in Colab · GitHub

• Boosting Classifier

  High-performance classification using Boosting.

  Open in Colab · GitHub

Clustering

• KMeans Clustering

  Standard K-Means clustering implementation.

  Open in Colab · GitHub

• Gnostic Local Clustering

  Clustering based on local Gnostic properties.

  Open in Colab · GitHub

Forecasting

• AutoRegressor (AR)

  Time series forecasting with AutoRegression.

  Open in Colab · GitHub

• ARIMA

  Forecasting with ARIMA models.

  Open in Colab · GitHub

• SARIMA

  Seasonal ARIMA for complex time series.

  Open in Colab · GitHub

MLflow

• MLflow Integration

  Track experiments and manage models with MLflow.

  Open in Colab · GitHub

• Deep Learning Models

  Advanced neural network architectures using Machine Gnostics.

  Coming soon!

  Read more about MagE

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Notebooks Source

All tutorials are hosted in our GitHub repository. You can download them directly or run them in Colab.