Implementing XAI: Practical Workflow with Python

Intermediate

🧪 Implementing Explainability with SHAP: A Practical Workflow

Implementing explainability involves selecting techniques tailored to your model and data.
Below is a step-by-step example using SHAP to interpret a Random Forest Classifier:

🧾 Python Code Example

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import shap

# Load dataset
data = load_iris()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Initialize SHAP explainer
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

# Visualize local explanation for a single prediction
shap.force_plot(explainer.expected_value[1], shap_values[1][0], X_test[0], feature_names=data.feature_names)

# Global feature importance
shap.summary_plot(shap_values[1], X_test, feature_names=data.feature_names)

🔍 What This Workflow Demonstrates

  • 🧠 Local Explanations: SHAP’s force_plot visualizes feature impact on a single prediction
  • 🌐 Global Interpretability: summary_plot reveals overall feature importance across all predictions

🎯 Best Practices

  • 📌 Tailor explainability tools to your model type
  • 🖼️ Use visualization tools to communicate results clearly
  • 🧭 Ensure outputs are both interpretable and actionable

➡️ This approach enhances trust, supports validation, and improves model transparency.