Quick Start Guide

# Import your favorite ML library
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import cloudexplain

# Train your model as usual (example with scikit-learn)
# Load your data
X, y = load_your_data()  # Replace with your data loading
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train your model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Define feature descriptions (optional but recommended)
feature_descriptions = {
    "feature_1": "Description of what feature 1 represents",
    "feature_2": "Description of what feature 2 represents",
    # Add descriptions for all your features
}

# Explain your model with CloudExplain
with cloudexplain.azure.explain(
    model=model,
    X=X_test,
    y=y_test,
    model_version="1.0.0",
    model_name="My Awesome Model",
    model_description="A model that predicts something important.",
    explanation_name="My Model Explanation 2025-06-14",
    explanation_env="prod",
    data_source="my dataset",
    ml_type="binary_classification",  # or "multi_class_classification", "regression"
    is_higher_output_better=True,
    feature_descriptions=feature_descriptions,
    baseline_data=X_train,
    api_token="your_api_token_here",  # Get this from /dashboards/analytics/tokens
    function_url="https://your-env-execute-containers.azurewebsites.net/api/upload_via_token"
) as run:
    # Your model is now explained!
    print(f"Explanation completed! ID: {run.explanation_id}")
    print("View your results in the CloudExplain dashboard")
    
    # The explanation is automatically uploaded to the cloud
    # You can access detailed insights in your dashboard