ML Model Explanation

Model explainability makes machine learning decisions transparent and interpretable, enabling trust, compliance, debugging, and actionable insights from predictions.

Explanation Techniques

• Feature Importance: Global feature contribution to predictions
• SHAP Values: Game theory-based feature attribution
• LIME: Local linear approximations for individual predictions
• Partial Dependence Plots: Feature relationship with predictions
• Attention Maps: Visualization of model focus areas
• Surrogate Models: Simpler interpretable approximations

Explainability Types

• Global: Overall model behavior and patterns
• Local: Explanation for individual predictions
• Feature-Level: Which features matter most
• Model-Level: How different components interact

Python Implementation

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.inspection import partial_dependence, permutation_importance
import warnings
warnings.filterwarnings('ignore')

print("=== 1. Feature Importance Analysis ===")

# Create dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10,
                          n_redundant=5, random_state=42)
feature_names = [f'Feature_{i}' for i in range(20)]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train models
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

gb_model = GradientBoostingClassifier(n_estimators=100, random_state=42)
gb_model.fit(X_train, y_train)

# Feature importance methods
print("\n=== Feature Importance Comparison ===")

# 1. Impurity-based importance (default)
impurity_importance = rf_model.feature_importances_

# 2. Permutation importance
perm_importance = permutation_importance(rf_model, X_test, y_test, n_repeats=10, random_state=42)

# Create comparison dataframe
importance_df = pd.DataFrame({
    'Feature': feature_names,
    'Impurity': impurity_importance,
    'Permutation': perm_importance.importances_mean
}).sort_values('Impurity', ascending=False)

print("\nTop 10 Most Important Features (by Impurity):")
print(importance_df.head(10)[['Feature', 'Impurity']])

# 2. SHAP-like Feature Attribution
print("\n=== SHAP-like Feature Attribution ===")

class SimpleShapCalculator:
    def __init__(self, model, X_background):
        self.model = model
        self.X_background = X_background
        self.baseline = model.predict_proba(X_background.mean(axis=0).reshape(1, -1))[0]

    def predict_difference(self, X_sample):
        """Get prediction difference from baseline"""
        pred = self.model.predict_proba(X_sample)[0]
        return pred - self.baseline

    def calculate_shap_values(self, X_instance, n_iterations=100):
        """Approximate SHAP values"""
        shap_values = np.zeros(X_instance.shape[1])
        n_features = X_instance.shape[1]

        for i in range(n_iterations):
            # Random feature subset
            subset_mask = np.random.random(n_features) > 0.5

            # With and without feature
            X_with = X_instance.copy()
            X_without = X_instance.copy()
            X_without[0, ~subset_mask] = self.X_background[0, ~subset_mask]

            # Marginal contribution
            contribution = (self.predict_difference(X_with)[1] -
                          self.predict_difference(X_without)[1])

            shap_values[~subset_mask] += contribution / n_iterations

        return shap_values

shap_calc = SimpleShapCalculator(rf_model, X_train)

# Calculate SHAP values for a sample
sample_idx = 0
shap_vals = shap_calc.calculate_shap_values(X_test[sample_idx:sample_idx+1], n_iterations=50)

print(f"\nSHAP Values for Sample {sample_idx}:")
shap_df = pd.DataFrame({
    'Feature': feature_names,
    'SHAP_Value': shap_vals
}).sort_values('SHAP_Value', key=abs, ascending=False)

print(shap_df.head(10)[['Feature', 'SHAP_Value']])

# 3. Partial Dependence Analysis
print("\n=== 3. Partial Dependence Analysis ===")

# Calculate partial dependence for top features
top_features = importance_df['Feature'].head(3).values
top_feature_indices = [feature_names.index(f) for f in top_features]

pd_data = {}
for feature_idx in top_feature_indices:
    pd_result = partial_dependence(rf_model, X_test, [feature_idx])
    pd_data[feature_names[feature_idx]