BlackBox PrecisionCore SDK
Unlock high-stakes performance with Explainable AI. SHAP and LIME integration for medical diagnostics, autonomous systems, and mission-critical applications.
Key Features
Everything you need to build transparent, accountable AI systems without sacrificing performance.
SHAP Integration
Theoretical gold standard for feature attribution with mathematical guarantees for regulatory compliance.
LIME Integration
Fast, intuitive local explanations perfect for real-time operational oversight.
Global & Local Explanations
Support for both comprehensive auditing and operational oversight at the instance level.
High-Stakes Ready
Built for mission-critical applications where errors carry catastrophic consequences.
Comprehensive Utilities
Tools for validation, aggregation, audit trails, and explanation comparison.
Model Auditing
Perform comprehensive model auditing to detect biases and validate system behavior.
Quick Start
Get started with BlackBox Precision in minutes. Choose your preferred installation method.
npm install blackboxpcsBasic Usage
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from blackboxpcs import BlackBoxPrecision, ExplanationType
# Train a black box model
X_train = np.random.rand(100, 10)
y_train = np.random.randint(0, 2, 100)
model = RandomForestClassifier()
model.fit(X_train, y_train)
# Initialize BlackBox Precision framework
bbp = BlackBoxPrecision(
model=model,
explainer_type=ExplanationType.BOTH,
feature_names=[f"feature_{i}" for i in range(10)]
)
# Generate local explanation
X_test = np.random.rand(1, 10)
result = bbp.explain_local(X_test)
print("Prediction:", result["predictions"])
print("SHAP Explanation:", result["explanations"]["shap"])
print("LIME Explanation:", result["explanations"]["lime"])Use Cases
Built for high-stakes environments where transparency and performance are non-negotiable.
Medical Diagnostics
Challenge
Deploying high-accuracy diagnostic AI without clinical justification
Solution
SHAP provides verifiable explanations for every diagnosis
Impact
- Clinical trust
- Regulatory compliance
- Audit trails
Autonomous Systems
Challenge
Validating safety-critical, split-second decisions
Solution
LIME provides instant explanations for real-time validation
Impact
- Safety verification
- Compliance
- Post-incident analysis
Financial Systems
Challenge
Explaining credit decisions and fraud detection
Solution
Combined SHAP and LIME for comprehensive explanations
Impact
- Regulatory compliance
- Customer trust
- Bias detection