Coefficients vs. permutation importance

Now, you will compare the patterns identified by permutation importance with the model coefficients from a logistic regression trained on the heart disease dataset. A helper function plot_importances() is called at the end of the script to plot importances on the same plot.

X containing the features and y containing the labels, and the logistic regression model have been pre-loaded for you. matplotlib.pyplot has been imported as plt.

Latihan ini adalah bagian dari kursus

Explainable AI in Python

Petunjuk latihan

Compute the coefficients of the logistic regression model.
Compute the permutation importance with 20 repeats using a random_state of 1.
Compute the average permutation importance across all repeats.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

from sklearn.inspection import permutation_importance

# Extract and store model coefficients
coefficients = ____

# Compute permutation importance on the test set
perm_importance = ____

# Compute the average permutation importance
avg_perm_importance = ____

plot_importances(coefficients, avg_perm_importance)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Explainable AI in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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

Latihan Saat Ini

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 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