Editing Explainable Ai (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Computing SHAP values for a gradient boosting model:'''
<syntaxhighlight lang="python">
import shap
import xgboost as xgb
import pandas as pd
from sklearn.model_selection import train_test_split

# Train a model
X, y = shap.datasets.adult()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = xgb.XGBClassifier(n_estimators=100, max_depth=4)
model.fit(X_train, y_train)

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

# Global feature importance
shap.summary_plot(shap_values, X_test, plot_type="bar")

# Local explanation for one prediction
shap.waterfall_plot(shap.Explanation(
    values=shap_values[0],
    base_values=explainer.expected_value,
    data=X_test.iloc[0],
    feature_names=X_test.columns.tolist()
))
</syntaxhighlight>

'''Grad-CAM for CNN visual explanation:'''
<syntaxhighlight lang="python">
import torch, torch.nn.functional as F
from torchvision.models import resnet50, ResNet50_Weights

model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
model.eval()
# Hook on last conv layer to capture gradients and activations
activations, gradients = {}, {}
model.layer4[-1].register_forward_hook(lambda m, i, o: activations.update({'feat': o}))
model.layer4[-1].register_full_backward_hook(lambda m, gi, go: gradients.update({'feat': go[0]}))
output = model(input_tensor)
model.zero_grad()
output[0, output.argmax()].backward()
cam = (gradients['feat'] * activations['feat']).mean(dim=[2, 3], keepdim=True)
cam = F.relu(cam).squeeze()  # Heatmap
</syntaxhighlight>

; XAI method by context
: '''Tabular, tree models''' → SHAP TreeExplainer (exact, fast)
: '''Tabular, any model''' → SHAP KernelExplainer, LIME
: '''Image classification''' → Grad-CAM, SHAP GradientExplainer, Integrated Gradients
: '''NLP''' → SHAP on tokenized input, attention visualization, LIME for text
: '''High-stakes decisions''' → Counterfactual explanations, contrastive explanations
: '''Regulatory compliance''' → Model cards, audit trails, decision logs
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