Kernel explainer for MLPRegressor

Given your familiarity with the admissions dataset, you will employ SHAP's Kernel Explainer to explain an MLPRegressor trained on this data. This method will allow you to critically assess how different features impact the model's predictions and verify these insights against your existing understanding of the dataset.

X containing the predictors and y containing the admission decisions, along with the pre-trained MLPRegressor model, have been pre-loaded for you.

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Explainable AI in Python

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Istruzioni dell'esercizio

Create a SHAP Kernel Explainer using the MLPRegressor model and a k-means summary of 10 samples from X.
Generate shap_values for X.
Compute the mean absolute SHAP values to identify key factors affecting admissions.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

import shap

# Create a SHAP Kernel Explainer
explainer = ____

# Calculate SHAP values
shap_values = ____

# Calculate mean absolute SHAP values
mean_abs_shap = ____

plt.bar(X.columns, mean_abs_shap)
plt.title('Mean Absolute SHAP Values for MLPRegressor')
plt.xticks(rotation=45)
plt.show()

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Questo esercizio fa parte del corso

Explainable AI in Python

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Begin your journey by exploring the foundational concepts of explainable AI. Learn how to extract decision rules from decision trees. Derive and visualize feature importance using linear and tree-based models to gain insights into how these models make predictions, enabling more transparent decision-making.

Exercise 1: Introduction to explainable AI Exercise 2: Decision trees vs. neural networks Exercise 3: Model-agnostic vs. model-specific explainability Exercise 4: Explainability in linear models Exercise 5: Computing feature impact with linear regression Exercise 6: Computing feature impact with logistic regression Exercise 7: Explainability in tree-based models Exercise 8: Computing feature importance with decision trees Exercise 9: Computing feature importance with random forests

Unlock the power of model-agnostic techniques to discern feature influence across various models. Employ permutation importance and SHAP values to analyze how features impact model behavior. Explore SHAP visualization tools to make explainability concepts more comprehensible.

Exercise 1: Permutation importance Exercise 2: Permutation importance for MLPClassifier Exercise 3: Coefficients vs. permutation importance Exercise 4: SHAP explainability Exercise 5: Finding key medical charge predictors with SHAP Exercise 6: Finding key heart disease predictors with SHAP Exercise 7: SHAP kernel explainer Exercise 8: Kernel explainer for MLPRegressor

Esercizio in corso

Exercise 9: Kernel explainer for MLPClassifier Exercise 10: SHAP vs. model-specific approaches Exercise 11: Visualizing SHAP explainability Exercise 12: Feature Importance plots for admissions analysis Exercise 13: Analyzing feature effects with beeswarm plots Exercise 14: Assessing impact with partial dependence plots

Dive into local explainability, and explain individual predictions. Learn to leverage SHAP for local explainability. Master LIME to reveal the specific factors influencing single outcomes, whether through textual, tabular, or image data.

Exercise 1: Local explainability with SHAP Exercise 2: Global vs. local explainability Exercise 3: SHAP for explaining income levels Exercise 4: Local explainability with LIME Exercise 5: Interpreting regressors locally Exercise 6: Interpreting classifiers locally Exercise 7: Text and image explainability with LIME Exercise 8: Explaining sentiment analysis predictions Exercise 9: Explaining food image predictions

Explore advanced topics in explainable AI by assessing model behaviors and the effectiveness of explanation methods. Gain proficiency in evaluating the consistency and faithfulness of explanations, delve into unsupervised model analysis, and learn to clarify the reasoning processes of generative AI models like ChatGPT. Equip yourself with techniques to measure and enhance explainability in complex AI systems.

Exercise 1: Explainability metrics Exercise 2: Evaluating SHAP explanation consistency Exercise 3: Evaluating faithfulness with LIME Exercise 4: Explaining unsupervised models Exercise 5: Feature impact on cluster quality Exercise 6: Feature importance in clustering with ARI Exercise 7: Explaining chat-based generative AI models Exercise 8: Chain-of-thought to discover reasoning Exercise 9: Self-consistency to assess confidence Exercise 10: Congratulations