Beginner Explanation
Imagine you’re baking a cake, and you want to know how much each ingredient affects the taste. ExCIR is like a special tasting tool that helps you figure out how different ingredients work together to make the cake delicious, rather than just tasting each one separately. This way, you can also save time and effort in your baking process, making it easier to create the perfect cake every time!Technical Explanation
ExCIR (Exponential Correlation-aware Importance Ranking) is a method that measures the importance of features in machine learning models while considering how features interact with each other. Traditional methods may overlook these interactions, leading to misleading attributions. ExCIR calculates an attribution score by analyzing the correlation between features and their co-movement in relation to model outputs. This is achieved through a computationally efficient algorithm that reduces the complexity of the calculations. An example implementation in Python using NumPy could look like this: “`python import numpy as np def excir(X, y): # Calculate correlation matrix corr_matrix = np.corrcoef(X, rowvar=False) # Compute ExCIR scores scores = np.exp(np.sum(corr_matrix, axis=1)) return scores “`Academic Context
ExCIR builds upon the foundational concepts of feature importance and attribution in machine learning. Traditional attribution methods like SHAP and LIME often assume independence among features, which can lead to inaccurate importance scores when features are correlated. ExCIR addresses this limitation by incorporating correlation-aware mechanisms into the attribution process. The theoretical framework can be traced back to works on cooperative game theory and Shapley values, which provide a basis for fair distribution of contributions among correlated features. Key papers include ‘A Unified Approach to Interpreting Model Predictions’ (Lundberg & Lee, 2017) and ‘Explaining Explanations: An Overview of Interpretability of Machine Learning’ (Lipton, 2016).Code Examples
Example 1:
import numpy as np
def excir(X, y):
# Calculate correlation matrix
corr_matrix = np.corrcoef(X, rowvar=False)
# Compute ExCIR scores
scores = np.exp(np.sum(corr_matrix, axis=1))
return scoresExample 2:
# Calculate correlation matrix
corr_matrix = np.corrcoef(X, rowvar=False)
# Compute ExCIR scores
scores = np.exp(np.sum(corr_matrix, axis=1))
return scoresExample 3:
import numpy as np
def excir(X, y):
# Calculate correlation matrix
corr_matrix = np.corrcoef(X, rowvar=False)Example 4:
def excir(X, y):
# Calculate correlation matrix
corr_matrix = np.corrcoef(X, rowvar=False)
# Compute ExCIR scores
scores = np.exp(np.sum(corr_matrix, axis=1))View Source: https://arxiv.org/abs/2511.16482v1