Universal ML Framework

Contents:

  • Installation
    • Install from PyPI
    • Install from Source
    • Requirements
    • Verify Installation
  • Quick Start Guide
    • Basic Usage
      • Classification Problem
      • Regression Problem
    • Quick Setup Functions
      • One-liner Classification
      • One-liner Regression
    • Generate Sample Data
    • Customization Options
      • Exclude Columns
      • Custom Feature Types
    • What Happens Automatically
  • Titanic Case Study
    • Dataset Overview
    • Implementation
      • Complete Titanic Prediction Script
      • What Happens Automatically
    • Expected Results
      • Typical Performance Metrics
      • Feature Importance Analysis
    • Output Files
      • Generated Files
    • Advanced Usage
      • Custom Feature Engineering
      • Exclude Irrelevant Features
    • Performance Comparison
      • Framework vs Manual Implementation
    • Key Insights
      • Why This Works Well
      • Lessons Learned
  • Architecture & Design
    • Core Components
      • UniversalMLPipeline Class
      • Key Attributes
    • Pipeline Workflow
    • Design Principles
      • Universal Design
      • Automation First
      • Performance Optimization
      • Production Ready
      • Extensibility
    • Performance Characteristics
      • Speed Benchmarks
      • Memory Usage
      • Scalability Features
    • Error Handling Strategy
      • Data Quality Validation
      • Graceful Degradation
      • Validation Framework
  • Advanced Usage
    • Performance Optimization
      • Fast Mode for Large Datasets
      • Multi-Core Processing
    • Hyperparameter Tuning Strategies
      • Grid Search
      • Random Search
      • Bayesian Optimization
    • Custom Feature Engineering
      • Custom Preprocessing Functions
      • Manual Feature Selection
    • Advanced Configuration
      • Verbose Mode
      • Custom ID Columns
      • Column Exclusion
    • Model Customization
      • Adding Custom Models
      • Custom Parameter Grids
    • Production Deployment
      • Model Loading and Inference
      • Batch Processing
      • Model Monitoring
    • Troubleshooting
      • Common Issues and Solutions
      • Performance Monitoring
  • API Reference
    • Core Classes
      • UniversalMLPipeline
    • Helper Functions
      • Quick Setup Functions
    • Data Generation
      • DataGenerator
    • Method Details
      • Main Pipeline Methods
  • Examples
    • Customer Churn Classification
    • House Price Prediction
    • Sales Forecasting
    • Using Predefined Configurations
    • Custom Feature Engineering
    • Batch Processing Multiple Datasets
    • Working with Your Own Data
    • Loading Saved Models
  • Troubleshooting Guide
    • Common Issues and Solutions
      • Installation Issues
      • Data Loading Problems
      • Memory and Performance Issues
      • Feature Detection Issues
      • Model Training Problems
      • Prediction Issues
    • Error Messages and Solutions
      • Scikit-learn Related Errors
      • Pandas Related Errors
    • Performance Optimization Tips
      • Speed Up Training
      • Reduce Memory Usage
      • Debug Mode
    • Getting Help
      • Check Documentation
      • Common Debugging Steps
      • Report Issues
Universal ML Framework
  • Search

© Copyright 2024, Fathan Akram.

Built with Sphinx using a theme provided by Read the Docs.