Model = LinearRegression(fit_intercept=True) # Create a linear regression model based on the positioning of the data and Intercept, and predict a Best Fit: In this example, a linear regression model is created with random data, and an estimated regression line is displayed: # Import the packages and classes needed for this example:įrom sklearn.linear_model import LinearRegression The sum of all Shapley values should be the difference between the predictions and average value of the model. In this post, we’ll find Shapley values for each variable in a regression in order to try and find the correct weight for it. In practice, Shapley value regression attempts to resolve a weakness in linear regression reliability when predicting variables that have moderate to high correlation. The Shapley value is a concept in cooperative game theory, and can be used to help explain the output of any machine learning model. This post will show you how to make predictions using a variety of algorithms, including: It includes many supervised and unsupervised algorithms that can be used to analyze datasets and make predictions about the data. Scikit-Learn is one of the most useful Machine Learning (ML) libraries in Python.
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