Interpreting ML Book Cards

Question 1

What does each coefficient represent in a linear model?

Answer 1

Each coefficient quantifies the change in the outcome variable for a one-unit change in that feature, assuming other variables are held constant.

Question 2

Why is standardization important in linear models?

Answer 2

Standardization (scaling features to have zero mean and unit variance) helps in comparing the relative importance of features by making coefficients comparable.

Question 3

What are the key assumptions for interpreting linear models?

Answer 3

Linearity, no multicollinearity, homoscedasticity (constant variance of errors), and normality of residuals.

Question 4

How does high multicollinearity affect linear models?

Answer 4

High multicollinearity can make coefficients unstable and inaccurate, which can be mitigated by using techniques like ridge or lasso regression.

Question 5

What is the structure of a decision tree?

Answer 5

Decision trees split data based on feature values to create nodes and branches, leading to leaf nodes that provide the final prediction.

Question 6

How do decision trees handle interpretability?

Answer 6

Decision trees use if-then-else conditions that correspond to paths from the root to leaf nodes, making them easy to visualize and understand.

Question 7

What is a common limitation of decision trees?

Answer 7

Decision trees can overfit the data, capturing noise rather than underlying patterns, which can be mitigated by pruning.

Question 8

What is LIME and what does it do?

Answer 8

LIME (Local Interpretable Model-agnostic Explanations) explains predictions of any black-box model by creating an interpretable local approximation of the model around a specific instance.

Question 9

How does LIME create explanations?

Answer 9

LIME perturbs the instance by making small random changes to its feature values and fits a simple model (e.g., linear) to approximate the complex model’s behavior around that instance.

Question 10

What are some limitations of LIME?

Answer 10

LIME’s explanations are local, not global, and its effectiveness depends on how well the perturbed data samples the local space of the instance.

Question 11

What are Shapley values?

Answer 11

Shapley values are derived from game theory to fairly attribute the output of a model to its input features, showing each feature’s contribution to a prediction.

Question 12

What are the key properties of Shapley values?

Answer 12

Efficiency, symmetry, dummy, and additivity—these properties ensure fair attribution of the model’s output to features.

Question 13

What are the limitations of Shapley values?

Answer 13

They are computationally intensive, especially with many features, and assume feature independence, which may not always hold.

Question 14

What is SHAP and how does it relate to Shapley values?

Answer 14

SHAP (SHapley Additive exPlanations) is an implementation of Shapley values tailored for machine learning models, offering efficient computation and a unified interpretation framework.

Question 15

How does SHAP provide interpretations?

Answer 15

SHAP values represent additive feature attribution, decomposing a prediction into contributions from each feature, with specific methods like Kernel SHAP, Tree SHAP, and Deep SHAP.

Question 16

What are some visualization tools used with SHAP?

Answer 16

Force plots, summary plots, and dependence plots are used to visualize how features contribute to individual predictions and overall model behavior.

Question 17

What are the benefits of using SHAP?

Answer 17

SHAP provides consistent and unbiased explanations, handles feature interactions, and has fast computation methods, especially for tree-based models.

Question 18

What are the limitations of SHAP?

Answer 18

SHAP values can be overwhelming with many features, require good understanding of data and model, and are sensitive to data distributions and feature independence assumptions.

Question 19

How do you interpret the coefficient of a feature in a standardized linear model?

Answer 19

The coefficient represents the number of standard deviations the outcome variable will change for a one standard deviation increase in the feature, assuming all other variables are constant.

Question 20

What is homoscedasticity and why is it important in linear models?

Answer 20

Homoscedasticity means that the variance of the errors is constant across all levels of the independent variables; it’s important because it ensures that the model’s predictions are reliable and unbiased across the range of data.

Question 21

What methods can be used to handle multicollinearity in linear models?

Answer 21

Ridge regression (adds L2 regularization) and Lasso regression (adds L1 regularization) are common methods to stabilize coefficients and reduce multicollinearity effects.

Question 22

What is the Gini impurity in decision trees?

Answer 22

Gini impurity measures the likelihood of an incorrect classification of a randomly chosen element if it were randomly classified according to the distribution of labels in the node. A lower Gini impurity indicates a purer node.

Question 23

How do decision trees determine feature importance?

Answer 23

Decision trees determine feature importance based on how effectively each feature splits the data, using metrics like Gini impurity or information gain. Features that result in more homogeneous splits are deemed more important.

Question 24

What is pruning in decision trees and why is it used?

Answer 24

Pruning involves cutting off parts of the tree that have little predictive power to reduce overfitting and improve the tree’s ability to generalize to new data.

Question 25

What is information gain and how is it used in decision trees?

Answer 25

Information gain measures the reduction in entropy or uncertainty from a node split. It is used in decision trees to choose the best feature that splits the data most effectively.

Question 26

What are surrogate models in the context of LIME?

Answer 26

Surrogate models in LIME are simpler models (like linear models) that approximate the behavior of complex models locally around a specific instance to make the model’s predictions interpretable.

Question 27

How does LIME handle categorical features during perturbation?

Answer 27

LIME handles categorical features by sampling values from the observed distribution of the feature in the dataset, ensuring the perturbed data remains realistic and relevant to the specific instance.

Question 28

Why is it important to choose appropriate perturbations in LIME?

Answer 28

Appropriate perturbations are crucial in LIME because they ensure that the local surrogate model accurately captures the complex model’s decision boundary around the instance being explained.

Question 29

What are the computational challenges associated with Shapley values?

Answer 29

Calculating Shapley values requires evaluating the model’s output across all possible subsets of features, making it computationally expensive, especially for models with many features or complex interactions.

Question 30

How does Kernel SHAP differ from Tree SHAP?

Answer 30

Kernel SHAP is model-agnostic and uses a weighted linear regression approach to estimate Shapley values, while Tree SHAP is optimized specifically for tree-based models, providing exact Shapley values by leveraging the tree structure.

Question 31

How does Deep SHAP extend Shapley values to neural networks?

Answer 31

Deep SHAP combines Shapley values with DeepLIFT, a method for feature attribution in neural networks, to provide explanations for deep learning models by backpropagating contributions through the network layers.

Question 32

What does a force plot in SHAP show?

Answer 32

A force plot in SHAP shows how each feature pushes the prediction higher or lower compared to the average prediction, visually representing the additive contributions of each feature to the final prediction.

Question 33

How do SHAP dependence plots help in interpreting feature interactions?

Answer 33

SHAP dependence plots show the relationship between SHAP values of a feature and the feature’s value, allowing you to see how the impact of a feature changes in relation to other features, revealing interaction effects.

Question 34

What makes SHAP values unbiased and consistent for feature attribution?

Answer 34

SHAP values satisfy properties like efficiency, symmetry, dummy, and additivity, which ensure fair and consistent attribution of the prediction to input features across different subsets and combinations of features.

Question 35

How do SHAP summary plots provide insights into model behavior?

Answer 35

SHAP summary plots aggregate SHAP values across all instances in the dataset, showing the distribution and magnitude of feature impacts, with colors indicating feature values, helping to identify key features and patterns in the data.