What are Shap interaction values?

What are Shap interaction values?

Ensembles, it says “SHAP interaction values can be interpreted as the difference between the A. SHAP values for feature i when feature j is present and B. the SHAP values for feature i when feature j is absent.”

What is shap Expected Value?

The base value or the expected value is the average of the model output over the training data X_train . It is the base value used in the following plot.

Can you add Shap values?

1 Answer. From Lundberg, package author: “The short answer is yes, you can add up SHAP values across the columns to get the importance of a whole group of features (just make sure you don’t take the absolute value like we do when going across rows for global feature importance).

What is Shap method?

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations).

What does a negative Shap value mean?

In this case we can see that for values of RM below 7 (x-axis), the SHAP values (y-axis) are virtually always negative, which means lower values of this feature push the prediction value down. Also, if you have an RM equal to 6, then you can have a SHAP value between -2.5 and 0, depending on the value of RAD.

How understand features are important?

The idea is simple: after evaluating the performance of your model, you permute the values of a feature of interest and reevaluate model performance. The observed mean decrease in performance — in our case area under the curve — indicates feature importance.

What does a negative SHAP value mean?

How do you read Shapley value?

The interpretation of the Shapley value is: Given the current set of feature values, the contribution of a feature value to the difference between the actual prediction and the mean prediction is the estimated Shapley value.

How long does Shap take to run?

Running SHAP on a knn model built on the Boston Housing dataset took over an hour, which is a tough pill to swallow. We can get that down to three minutes if we sacrifice some accuracy and reliability by summarizing the data first with a k-means algorithm.

What is Shap package?

What does high Shap value mean?

In a nutshell, SHAP values are used whenever you have a complex model (could be a gradient boosting, a neural network, or anything that takes some features as input and produces some predictions as output) and you want to understand what decisions the model is making. Predictive models answer the “how much”.

How are Shapley value calculated?

The Shapley value is computed by taking the average of difference from all combinations. Essentially, the Shapley value is the average marginal contribution of a feature considering all possible combinations. Therefore, in a practical scenario, the Shapley value can only be estimated using a subset of combinations.

When to use Shap values in a model?

In a nutshell, SHAP values are used whenever you have a complex model (could be a gradient boosting, a neural network, or anything that takes some features as input and produces some predictions as output) and you want to understand what decisions the model is making.

How is the importance of a Shapley value calculated?

Shapley values calculate the importance of a feature by comparing what a model predicts with and without the feature. However, since the order in which a model sees features can affect its predictions, this is done in every possible order, so that the features are fairly compared.

When to use Shap in a neural network?

In a nutshell, SHAP values are used whenever you have a complex model (could be a gradient boosting, a neural network, or anything that takes some features as input and produces some predictions as output) and you want to understand what decisions the model is making. Predictive models answer the “how much”. SHAP answers the “why”.

How is a sales score calculated in Shap?

Imagine a sales score model. A customer living in zip code “A1” with “10 purchases” arrives and its score is 95%, while other from zip code “A2” and “7 purchases” has a score of 60%. Each variable had its contribution to the final score.