User Guide

Comprehensive guides for using edaflow effectively in your data analysis and machine learning workflows.

• Data Quality & Cleaning
  • Overview
• How edaflow Splits Your Dataset: Training, Validation, and Test
• Further Resources & FAQ
• Choosing the Right Performance Visualization
• Overview
• Data Validation: A Critical First Step
• Best practice: Aim for a high data quality score to ensure robust, reliable model results.
  • About cv_folds
  • Other Key Parameters for Model Comparison
  • Choosing Metrics by Problem Type
  • Practical Examples: Metric Selection
• Complete ML Workflow Example
• Individual Function Examples
  • Configuration Functions
  • This workflow helps you make informed, data-driven choices about which model to use in production or further tuning.
  • Hyperparameter Tuning Functions
• Why is it important?
• How does it work?
  • Performance Visualization Functions
  • Model Artifacts Functions
• Best Practices
• This approach ensures you have a solid reference point and helps you build more robust, trustworthy machine learning solutions.
• Widely Used Model Types in Machine Learning
• Refer to scikit-learn and the respective library documentation for more details and advanced options.
• What’s Next After Training the Model?

Overview

The edaflow User Guide is organized into five main sections:

Data Quality & Cleaning

Learn how to assess data quality, handle missing values, convert data types, and prepare your data for analysis.

• Missing data analysis and visualization

• Categorical data insights and type conversion

• Data imputation strategies

• Outlier detection and handling

Visualization & Analysis

Explore edaflow’s comprehensive visualization capabilities for understanding your data.

• Distribution analysis with boxplots and histograms

• Interactive visualizations with Plotly

• Correlation and relationship analysis

• Advanced scatter matrix analysis

Machine Learning Workflows

Master the complete ML pipeline with edaflow’s comprehensive machine learning functions.

• ML experiment setup and data validation

• Multi-model comparison and ranking systems

• Hyperparameter optimization strategies

• Performance visualization and model artifacts

• Complete workflow examples and best practices

Advanced Features

Discover advanced features and customization options for power users.

• Custom thresholds and parameters

• Integration with other libraries

• Performance optimization tips

• Extension and customization

Best Practices

Learn recommended workflows and best practices for effective EDA and ML.

• Recommended EDA workflow

• Memory and performance considerations

• Jupyter notebook integration

• Troubleshooting common issues

Getting Started

If you’re new to edaflow, we recommend starting with the Quick Start Guide guide, then exploring each section of this user guide based on your specific needs.

For complete function documentation, see the API Reference.