modeva.TestSuite.interpret_llm_pc#
- TestSuite.interpret_llm_pc(dataset: str = 'test')#
Generate and visualize parallel coordinate plots for Local Linear Model (LLM) coefficients.
This function calculates LLM statistics, feature importance, and creates a parallel coordinate plot to visualize the relationships between features and their coefficients across different local linear models.
- Parameters:
dataset ({"main", "train", "test"}, default="test") – The data set used for calculating the explanation results.
- Returns:
A container object with the following components:
key: “llm_pc”
data: Name of the dataset used
model: Name of the model used
inputs: Input parameters
value: Dictionary containing:
”Names”: Feature names
”Bias & Weights”: LLM coefficients including bias terms
”STD_DEV”: Standard deviations of LLM predictions
”Importance”: Feature importance scores
options: Dictionary of visualizations configuration for a parallel coordinate plot where x-axis is feature names, and y-axis is the local linear coefficients. Run results.plot() to show this plot.
- Return type:
Examples
