De impact van features berekenen met lineaire regressie

Als data scientist bij een verzekeringsmaatschappij is het jouw taak om een lineair regressiemodel te bouwen én uit te leggen dat de verzekeringskosten schat op basis van features zoals leeftijd, BMI en rookstatus. Analyseer de coëfficiënten van het model om te bepalen wat de impact van elke feature is op de voorspellingen.

matplotlib.pyplot is geïmporteerd als plt, samen met MinMaxScaler. X_train en y_train zijn alvast voor je ingeladen.

Deze oefening maakt deel uit van de cursus

Explainable AI in Python

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Oefeninstructies

Normaliseer de trainingsgegevens X_train.
Fit het lineaire regressiemodel op de gestandaardiseerde trainingsgegevens.
Haal de coefficients uit het model.
Plot de coefficients voor de gegeven feature_names.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Standardize the training data
scaler = MinMaxScaler()
X_train_scaled = ____
model = LinearRegression()

# Fit the model
____

# Derive coefficients
coefficients = ____
feature_names = X_train.columns

# Plot coefficients
plt.bar(____, ____)
plt.show()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Explainable AI in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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: Introductie tot explainable AI Exercise 2: Decision trees vs. neural networks Exercise 3: Model-agnostisch vs. model-specifieke uitlegbaarheid Exercise 4: Verklaarbaarheid in lineaire modellen Exercise 5: De impact van features berekenen met lineaire regressie

Huidige oefening

Exercise 6: De impact van features berekenen met logistic regression Exercise 7: Verklaarbaarheid in boomgebaseerde modellen Exercise 8: Feature-importance berekenen met beslisbomen Exercise 9: Feature-importance berekenen met 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 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