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About Xplainable

What is Xplainable?

Xplainable is a transparent machine learning package that provides real-time explainability without sacrificing performance. It bridges the gap between accuracy and interpretability through novel algorithms designed specifically for complete transparency.

Overview​

Xplainable is a Python package that leverages explainable machine learning for fully transparent machine learning and advanced data optimization in production systems. Unlike traditional black-box models, xplainable provides real-time explainability without needing surrogate models like SHAP or LIME.

Key Features​

🔍 Real-time Explainability

Get instant explanations without fitting surrogate models. Our transparent algorithms provide explanations as part of the prediction process.

⚡ Rapid Refitting

Update model parameters on-the-fly, even for individual features. Fine-tune your models without complete retraining.

🎯 Dual Interface

Use either programmatic Python APIs or interactive Jupyter GUIs. Perfect for both data scientists and business users.

☁️ Cloud Integration

Deploy models to production in seconds with Xplainable Cloud. Full collaboration and model management features.

Core Capabilities​

Xplainable provides a comprehensive suite of tools for the entire machine learning lifecycle:

📊 Data Preprocessing​

  • Comprehensive preprocessing pipeline with 15+ transformers
  • Data quality scanning and health checks
  • Feature engineering and transformation
  • Pipeline persistence and reusability

🤖 Model Training​

  • XClassifier: Transparent binary classification
  • XRegressor: Transparent regression
  • Partitioned Models: Multi-segment modeling
  • Surrogate Models: Explain black-box models

🔧 Hyperparameter Optimization​

  • Bayesian optimization with Hyperopt
  • Evolutionary algorithms
  • Cross-validation with early stopping
  • Custom search spaces

📈 Visualization & Explainability​

  • Global, regional, and local explanations
  • Feature importance analysis
  • Waterfall plots and decision trees
  • Real-time model insights

🚀 Deployment​

  • One-click API deployments
  • Model versioning and management
  • A/B testing capabilities
  • Production monitoring

Why Choose Xplainable?​

The Transparency Advantage

Traditional ML requires choosing between accuracy and explainability. Xplainable eliminates this trade-off by providing transparent algorithms that match the performance of black-box models while maintaining complete interpretability.

Performance Without Compromise​

FeatureTraditional MLXplainable
Accuracy✅ High✅ High
Explainability❌ Post-hoc only✅ Real-time
Speed⚡ Fast training⚡ Fast + rapid refitting
Deployment🔧 Complex🚀 One-click
Collaboration📝 Code-only👥 GUI + API

Novel Algorithms​

Xplainable introduces several breakthrough concepts:

  1. Feature-wise Ensemble: Each feature gets its own decision tree, optimized for information gain
  2. Rapid Refitting: Update parameters without full retraining
  3. Transparent Architecture: No black-box components
  4. Real-time Explanations: Explanations are part of the prediction process

Who Uses Xplainable?​

👨‍💼 Business Users​

  • Domain experts who need to understand model decisions
  • Managers requiring transparent AI for compliance
  • Analysts building interpretable models

👩‍💻 Data Scientists​

  • ML engineers seeking explainable alternatives to XGBoost/LightGBM
  • Researchers working on interpretable AI
  • Teams requiring rapid model iteration

🏢 Organizations​

  • Financial services (regulatory compliance)
  • Healthcare (clinical decision support)
  • Manufacturing (process optimization)
  • Any industry requiring AI transparency

Getting Started​

Ready to Start?

Jump to our Installation Guide to get xplainable up and running in minutes, or explore our Python API documentation for detailed examples.

Quick Example​

import xplainable as xp
from xplainable.core.models import XClassifier

# Load data
data = xp.load_dataset('titanic')
X, y = data.drop('Survived', axis=1), data['Survived']

# Train transparent model
model = XClassifier()
model.fit(X, y)

# Get real-time explanations
model.explain()  # No surrogate models needed!

Architecture Overview​

Community & Support​

  • Documentation: Comprehensive guides and API reference
  • Examples: Real-world use cases and tutorials
  • Community: Active user community and support
  • Enterprise: Professional support and custom solutions
Learn More

Explore our tutorials for hands-on examples, or dive into the Python API documentation for detailed technical information.