Explaining sentiment analysis predictions

You are provided with a model that classifies product reviews as expressing positive or negative sentiment. Your task is to use LIME to identify which words in a given text_instance most influence the model's predictions.

The model_predict function for processing input texts is pre-loaded for you.

Latihan ini adalah bagian dari kursus

Explainable AI in Python

Petunjuk latihan

Create a LIME text explainer named explainer.
Generate an explanation for the model's prediction on the given text_instance featuring the top five features.
Display the top contributing words and their weights that influence the model's decision.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

from lime.lime_text import LimeTextExplainer

text_instance = "Amazing battery life and the camera quality is perfect! I highly recommend this smartphone."

# Create a LIME text explainer
explainer = ____

# Generate the explanation
exp = ____

# Display the explanation
____
plt.show()

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 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

Latihan Saat Ini

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