Generating SHAP Values for Random Data and Model

This code uses the SHAP library to generate a random dataset and a random model (logistic regression), then calculates and returns the SHAP values of the model on the random dataset.

Technology Stack : The code uses the SHAP library and technologies, including numpy, pandas, and shap.

Code Type : The type of code

Code Difficulty :

                
                    
import numpy as np
import shap
import pandas as pd

def random_shap_values(X, y):
    """
    Generate SHAP values for a random model and data.
    """
    # Create a random dataset
    X = np.random.rand(100, 10)
    y = np.random.randint(0, 2, 100)
    
    # Fit a random model (e.g., logistic regression)
    model = shap.LinearModel().fit(X, y)
    
    # Calculate SHAP values
    explainer = shap.Explainer(model, X)
    shap_values = explainer(X)
    
    # Return the SHAP values as a DataFrame
    shap_df = pd.DataFrame(shap_values.values, columns=X.columns)
    
    return shap_df

# Code Information