To get from coalitions of feature values to valid data instances, we need a function Lundberg, Scott M., and Su-In Lee. The estimation puts too much weight on unlikely instances. The plot below sorts features by the sum of SHAP value magnitudes over all samples, and uses SHAP values to show the distribution of the impacts each feature has on the model output. In this case, the output will be 3D data containing importance for each sample, time steps, features in that order. Passing the entire train data will give highly accurate values, but will be unreasonably expensive as the complexity of this method scales linearly with the number of data points.Below is a brief about what data is used and how model prediction is done. If we run SHAP for every instance, we get a matrix of Shapley values. The more 0's in the coalition vector, the smaller the weight in LIME. We will be using BSE share market data from yahoo finance. Since we want the global importance, we average the absolute Shapley values per feature across the data:We have the data, the target and the weights. A benefit of using ensembles of decision tree methods like gradient boosting is that they can automatically provide estimates of feature importance from a trained predictive model. The SHAP explanation method computes Shapley values from coalitional game theory. A player can also be a group of feature values. $\phi_{i,j}=\sum_{S\subseteq\setminus\{i,j\}}\frac{|S|!(M-|S|-2)!}{2(M-1)! Advances in Neural Information Processing Systems. SHAP weights the sampled instances according to the weight the coalition would get in the Shapley value estimation. From Consistency the Shapley properties Linearity, Dummy and Symmetry follow, as described in the Appendix of Lundberg and Lee.Next, we sort the features by decreasing importance and plot them. SHAP describes the following three desirable properties:Since SHAP computes Shapley values, all the advantages of Shapley values apply: SHAP has a \[f_x'(z')-f_x'(z_{\setminus{}j}')\geq{}f_x(z')-f_x(z_{\setminus{}j}')$The following figure shows the SHAP feature dependence for years on hormonal contraceptives:How can we use the interaction index? The features are ordered according to their importance.Next, we will look at SHAP explanations in action.The disadvantages of Shapley values also apply to SHAP: Shapley values can be misinterpreted and access to data is needed to compute them for new data (except for TreeSHAP).The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. In the SHAP paper, you will find discrepancies between SHAP properties and Shapley properties. Theoretically, number of combination is 2^n, where n is number of feature. The estimated coefficients of the model, the For images, the following figure describes a possible mapping function:You can cluster your data with the help of Shapley values. The intuition behind it is: We learn most about individual features if we can study their effects in isolation. All SHAP values have the same unit -- the unit of the prediction space. From the remaining coalition sizes, we sample with readjusted weights.The big difference to LIME is the weighting of the instances in the regression model. SHAP … It is equally important to derive which features considered important by the model to enhance business decisions.Below graph shows true and false predictions on test dataset time series. The resulting values would violate the Shapley axiom of Dummy, which says that a feature that does not contribute to the outcome should have a Shapley value of zero.The summary plot combines feature importance with feature effects. STDs and lower cancer risk could be correlated with more doctor visits). We can see that at which points, the model succeeded to predict the decision and where it fails.One advantage of deep learning over machine learning is, deep learning can deal with large amount of data with accuracy but DL is considered by people as a black box models and hence they are less preferred for business or economics modelling. Effects might be due to confounding (e.g. FIGURE 5.51: SHAP feature importance measured as the mean absolute Shapley values. FIGURE 5.54: SHAP feature dependence plot with interaction visualization. "A unified approach to interpreting model predictions." When we have enough budget left (current budget is K - 2M), we can include coalitions with two features and with M-2 features and so on. import shap explainer = shap.TreeExplainer(rf) shap_values = explainer.shap_values(X_test) shap.summary_plot(shap_values, X_test, plot_type="bar") Once SHAP values are computed, other plots can be done: Computing SHAP values can be computationally expensive. Let take auto loan (car loan) as an example. Shapley values tell us how to fairly distribute the "payout" (= the prediction) among the features. As explained well on github page, SHAP … In the summary plot, we see first indications of the relationship between the value of a feature and the impact on the prediction. Maybe, across all individuals, age was the most important feature, and younger people are much more likely to like computer games. LIME weights the instances according to how close they are to the original instance. Shapley values calculate the importance of a feature by comparing what a model predicts with and without the feature. Using LSTM model, I have got around 60% binary classification accuracy for buy and sell predictions. Normally, clustering is based on features. Small coalitions (few 1's) and large coalitions (i.e. For example, to automatically color the SHAP feature dependence plot with the strongest interaction:$\pi_{x}(z')=\frac{(M-1)}{\binom{M}{|z'|}|z'|(M-|z'|)}$The feature importance plot is useful, but contains no information beyond the importances. We average the values over all possible feature coalitions S, as in the Shapley value computation. But if Frank is a … many 1's) get the largest weights. The number of years with hormonal contraceptives was the most important feature, changing the predicted absolute cancer probability on average by 2.4 percentage points (0.024 on x-axis). The number of years with hormonal contraceptives was the most important feature, changing the predicted absolute cancer probability on average by 2.4 percentage points (0.024 on x-axis). We start with all possible coalitions with 1 and M-1 features, which makes 2 times M coalitions in total.