Explore essential concepts of feature importance and model explainability with this quiz designed to reinforce your understanding of interpretable machine learning, feature evaluation, and the significance of transparent AI models. Perfect for those looking to grasp the basics of explaining model predictions and identifying influential features in data-driven solutions.
Which term best describes how much a feature contributes to a model's prediction, such as feature X having greater influence than feature Y in classifying images?
Explanation: Feature importance quantifies the contribution of each feature to a model’s predictions, making it central to model explainability. Feature scaling refers to adjusting the range of features, not their influence. Feature extraction is about creating new features from existing ones, and feature coding relates to representing categories numerically. Only 'feature importance' measures relative influence on outcomes.
Which term describes the process of understanding how a machine learning model arrives at its decisions using techniques like decision trees or partial dependence plots?
Explanation: Model explainability refers to methods used to interpret and understand how models make predictions. Data augmentation is used to expand training data, which does not explain decisions. Overfitting is a modeling problem where a model learns noise instead of signal, unrelated to explainability. Cross-validation assesses model performance, not interpretability.
In the context of explainability, what does 'global interpretability' refer to when analyzing a model’s behavior?
Explanation: Global interpretability focuses on understanding model behavior across the entire dataset or all predictions, providing insights into which features generally matter most. Explaining individual predictions refers to local interpretability. Transforming variables and improving computation are unrelated to the interpretability scope.
When computing permutation importance for a trained model, what is the primary action taken with each feature to evaluate its importance?
Explanation: Permutation importance involves randomly shuffling each feature and measuring the resulting decrease in model performance to assess how much the model relies on that feature. Normalizing does not determine importance. Removing the feature changes the nature of the model; doubling the values alters the scale but not the measurement of importance.
Which type of model is generally considered inherently interpretable due to its transparent decision-making process, for example, showing how age and income lead to a loan approval?
Explanation: Decision trees are considered interpretable because their rules and splits clearly show how decisions are made. Neural networks have complex structures making them hard to interpret. K-nearest neighbor depends on data proximity and doesn't present transparent logic. Random forests are ensembles of trees and are less interpretable than single trees due to their complexity.
What do SHAP values provide when explaining individual model predictions in contexts like credit scoring?
Explanation: SHAP values explain how much each feature contributed to an individual model prediction, making them useful for local interpretability. They do not measure average accuracy or perform transformations or random sampling. Their primary benefit lies in providing detailed feature attribution for single predictions.
Why is model explainability especially important in applications like medical diagnosis or loan approvals?
Explanation: In high-stakes applications, explainability helps ensure that stakeholders can trust and understand the model’s decisions. While explainability does not guarantee better performance or faster prediction, and it does not relate to dataset size, its core value lies in transparency and accountability.
What is a main limitation of using feature importance to infer relationships between features and target variables, such as in predicting house prices?
Explanation: Feature importance typically reveals how correlated a feature is with the target, but it cannot indicate causality. It does not only measure categorical features or remove outliers. Also, feature importance can be computed for both numerical and categorical data, so the latter two options are incorrect.
What do partial dependence plots help visualize in a model, such as showing the effect of 'age' on predicted insurance costs?
Explanation: Partial dependence plots show how predicted outcomes change as one feature varies, holding other features constant. They do not display raw data distributions or hyperparameter settings. Variance across datasets is not their focus. They specifically provide insights into feature-outcome relationships.
In a simple linear regression predicting weight from height, how can you interpret the model’s coefficient for height?
Explanation: A linear regression coefficient represents the change in the predicted value (weight) for each one-unit increase in the feature (height), assuming all else is constant. It is not the product of the variables, nor a random value assigned arbitrarily. The average weight is unrelated to the coefficient's interpretation.