What is linear regression value?
A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).
What does a positive regression value mean?
The sign of a regression coefficient tells you whether there is a positive or negative correlation between each independent variable and the dependent variable. A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase.
When to use Shapley value regression in a game?
For a game where a group of players cooperate, and where the expected payoff is known for each subset of players cooperating, one can calculate the Shapley value for each player, which is a way of fairly determining the contribution of each player to the payoff. I assume in the regression case we do not know what the expected payoff is.
Which is the correct answer for Shapley values?
The answer is simple for linear regression models. The effect of each feature is the weight of the feature times the feature value. This only works because of the linearity of the model. For more complex models, we need a different solution.
How are Shapley values used in predictive models?
But computing Shapley values for model features is not entirely straightforward, because features in a model do not behave the same way as workers in a team. Specifically, a predictive model cannot typically handle one of its input features being simply removed in order to test how its output changes as a result.
What should I know about Shapley values in Python?
This is a living document, and serves as an introduction to the shap Python package. So if you have feedback or contributions please open an issue or pull request to make this tutorial better! Before using Shapley values to explain complicated models, it is helpful to understand how they work for simple models.