Implement Support for Regression Models #1

examples/regressionmodelsupport.py

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+import pandas as pd
+from explainableai import XAIWrapper
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LinearRegression
+from xgboost import XGBRegressor
+from sklearn.neural_network import MLPRegressor
+
+# Load your dataset
+df = pd.read_csv('your_dataset.csv')
+X = df.drop(columns=['target_column'])
+y = df['target_column']
+
+# Perform EDA
+XAIWrapper.perform_eda(df)
+
+# Create models
+models = {
+    'Random Forest': RandomForestRegressor(n_estimators=100, random_state=42),
+    'Linear Regression': LinearRegression(),
+    'XGBoost': XGBRegressor(n_estimators=100, random_state=42),
+    'Neural Network': MLPRegressor(hidden_layer_sizes=(100, 50), max_iter=1000, random_state=42)
+}
+
+# Create XAIWrapper instance and fit models
+xai = XAIWrapper()
+xai.fit(models, X, y)
+results = xai.analyze()
+
+# Print LLM explanation of results
+print(results['llm_explanation'])
+
+# Generate a comprehensive report
+xai.generate_report('xai_report.pdf')
+
+# Make a prediction with explanation
+new_data = {...}  # Dictionary of feature values
+prediction, probabilities, explanation = xai.explain_prediction(new_data)
+print(f"Prediction: {prediction}")
+print(f"Probabilities: {probabilities}")
+print(f"Explanation: {explanation}")