Feature Importance plots for admissions analysis

As part of the data science team at a university, your task is to evaluate which factors truly drive admissions decisions and which ones are less important. While the admissions committee knows CGPA plays a key role, they want to confirm this and uncover any other important factors that may influence outcomes. Using a RandomForestRegressor model, you'll visualize feature importance to clearly identify which aspects of applicants' profiles matter most and which have less of an impact on the decision process.

The shap library and the training data (X_train, y_train) have been pre-loaded for you.

Deze oefening maakt deel uit van de cursus

Explainable AI in Python

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Oefeninstructies

Derive the shap_values using a TreeExplainer.
Use the derived shap_values to plot the feature importances with a bar plot and analyze it.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

model = RandomForestRegressor(random_state=42)
model.fit(X_train, y_train)

# Derive shap values
explainer = ____
shap_values = ____

# Plot the feature importance plot
____

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Deze oefening maakt deel uit van de cursus

Explainable AI in Python

SkillTag.level.intermediateSkillTag.label

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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 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

Huidige oefening

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