What is the use of Shap?

What is SHAP? SHAP or SHAPley Additive exPlanations is a visualization tool that can be used for making a machine learning model more explainable by visualizing its output. It can be used for explaining the prediction of any model by computing the contribution of each feature to the prediction.

What do Shap values represent?

The shap_values is a 2D array. Each row belongs to a single prediction made by the model. Each column represents a feature used in the model. Each SHAP value represents how much this feature contributes to the output of this row’s prediction.

How do you interpret SHAP value summary?

How to interpret the shap summary plot?

1. The y-axis indicates the variable name, in order of importance from top to bottom. The value next to them is the mean SHAP value.
2. On the x-axis is the SHAP value.
3. Gradient color indicates the original value for that variable.
4. Each point represents a row from the original dataset.

Is Shap model agnostic?

LIME and SHAP are two popular model-agnostic, local explanation approaches designed to explain any given black-box classifier.

What is Shap analysis?

SHAP analysis can be applied to the data from any machine learning model. It gives an indication of the relationships that combine to create the model’s output and you can gain real insights into the relationships between operations on your production line or the behaviour of in-service vehicles.

What is Shap plot?

SHAP dependence plots are an alternative to partial dependence plots and accumulated local effects. While PDP and ALE plot show average effects, SHAP dependence also shows the variance on the y-axis. Especially in case of interactions, the SHAP dependence plot will be much more dispersed in the y-axis.

What are Shap features?

SHAP feature importance is an alternative to permutation feature importance. There is a big difference between both importance measures: Permutation feature importance is based on the decrease in model performance. SHAP is based on magnitude of feature attributions.

What is Shap in data science?

SHAP values (SHapley Additive exPlanations) is a method based on cooperative game theory and used to increase transparency and interpretability of machine learning models.

What does a negative SHAP value mean?

losing
On the right side, the local explanation summary shows the direction of the relationship between a variable and game outcome. Positive SHAP-values are indicative of winning, while negative SHAP-values are indicative of losing.

Is Shap reliable?

(b) SHAP gives global explanations and feature importance Local explanations as described in (a) can be put together to get a global explanation. And because of the axiomatic assumptions of SHAP, it turns out global SHAP explanations can be more reliable than other measures such as the Gini index.

What is the difference between lime and Shap?

Use LIME for single prediction explanation. Use SHAP for entire model (or single variable) explanation.

What is Shap used for in machine learning?

The goal of SHAP is to explain a machine learning model’s prediction by calculating the contribution of each feature to the prediction. The technical explanation is that it does this by computing Shapley values from coalitional game theory.

What is Shap and why is it useful?

The same can be said for feature importances of tree-based models, and this is why SHAP is useful for interpretability of models. Important: while SHAP shows the contribution or the importance of each feature on the prediction of the model, it does not evaluate the quality of the prediction itself.

Is Shap more reliable than other measures?

And because of the axiomatic assumptions of SHAP, it turns out global SHAP explanations can be more reliable than other measures such as the Gini index. In the example below, researchers are predicting mortality risk based on a collection of baseline variables.

How does Shap evaluate the quality of the prediction?

Important: while SHAP shows the contribution or the importance of each feature on the prediction of the model, it does not evaluate the quality of the prediction itself. Consider a coooperative game with the same number of players as the name of features.

How does Shap work with machine learning?

With SHAP, we are trying to explain an individual prediction. So let’s take the example of patient A and try to explain their probability of having a hospitalization. Let’s imagine that according to our machine learning model this probability is 27%.