permutation feature importance vs shap

The more 0s in the coalition vector, the smaller the weight in LIME. Each position on the x-axis is an instance of the data. This structure could be chosen in many ways, but for tabular data it is often helpful to build the structure from the redundancy of information between the input features about the output label. Permutation importance is easy to explain, implement, and use. The code and analysis of the experiment could be found in the repository of the project: To make you familiar with what is going on, Ill illustrate a single experiment. While others are universal, they could be applied to almost any model: methods such as SHAP values, permutation importances, drop-and-relearn approach, and many others. The interaction effect is the additional combined feature effect after accounting for the individual feature effects. Also, we may see that that correlation between actual features importances and calculated depends on the models score: higher the score lower the correlation (Figure 10 Spearman features rank correlation = f(models score)). But instead of relying on the conditional distribution, this example uses the marginal distribution. I refer to the original paper for details of TreeSHAP. License. For a more informative plot, we will next look at the summary plot. Permutation importance is a frequently used type of feature importance. Note that \(x_j'\) refers to the coalitions where a value of 0 represents the absence of a feature value. KernelSHAP estimates for an instance x the contributions of each feature value to the prediction. The function \(h_x\) maps 1s to the corresponding value from the instance x that we want to explain. Use SHAP values or built-in gain importance instead. The feature values of a data instance act as players in a coalition. for tabular data. Each feature value is a force that either increases or decreases the prediction. If S contains some, but not all, features, we ignore predictions of unreachable nodes. The big difference to LIME is the weighting of the instances in the regression model. If we add an L1 penalty to the loss L, we can create sparse explanations. SHAP feature importance is an alternative to permutation feature importance. Revision 45b85c18. For a more informative plot, we will next look at the summary plot. Compared to 0 years, a few years lower the predicted probability and a high number of years increases the predicted cancer probability. Now we need to create a target. Here, M is the maximum coalition size and \(|z'|\) the number of present features in instance z. To get the label, I rounded the result. Copyright 2018, Scott Lundberg. Although the models black box unboxing is an integral part of the model development pipeline, a study conducted by Harmanpreet et al. The process is repeated several times to reduce the influence of random permutations and scores or ranks are averaged across runs. Each feature weight was then divided by the sum of weights, making the sum of weights equal to one. Risk increasing effects such as STDs are offset by decreasing effects such as age. The difficulty is to compute distances between instances with such different, non-comparable features. When the permutation is repeated, the results might vary greatly. Features for the task are ready! SHAP is based on magnitude of feature attributions. I think this name was chosen, because for e.g. The availability and simplicity of the methods are making them golden hammer. To compute Shapley values, we simulate that only some feature values are playing (present) and some are not (absent). where Z is the training data. It is possible to create intentionally misleading interpretations with SHAP, which can hide biases 72. TreeSHAP can produce unintuitive feature attributions. In general the distinctions between these methods for tabular data are not large, though the Partition masker allows for much faster runtime and potentially more realistic manipulations of the model inputs (since groups of clustered features are masked/unmasked together). For example to explain an image, pixels can be grouped to superpixels and the prediction distributed among them. Overlapping points are jittered in y-axis direction, so we get a sense of the distribution of the Shapley values per feature. If we conditioned on all features if S was the set of all features then the prediction from the node in which the instance x falls would be the expected prediction. Cell link copied. The goal of clustering is to find groups of similar instances. Although calculation requires to make predictions on training data n_featurs times, it's not a substantial operation, compared to model retraining or precise SHAP values calculation. permutation feature importance vs shap. # train an XGBoost model (but any other model type would also work), # build a Permutation explainer and explain the model predictions on the given dataset, # get just the explanations for the positive class, # build a clustering of the features based on shared information about y, # above we implicitly used shap.maskers.Independent by passing a raw dataframe as the masker, # now we explicitly use a Partition masker that uses the clustering we just computed, Tabular data with independent (Shapley value) masking, Tabular data with partition (Owen value) masking. history 4 of 4. I conducted an experiment, which showed that permutation importance suffers the most from highly correlated features (among importances calculated using SHAP values and gain). If you are the data scientist creating the explanations, this is not an actual problem (it would even be an advantage if you are the evil data scientist who wants to create misleading explanations). We will use SHAP to explain individual predictions. And they proposed TreeSHAP, an efficient estimation approach for tree-based models. Dont use permute-and-relearn or drop-and-relearn approaches for finding important features. This is what we do below: Note that only the Relationship and Marital status features share more that 50% of their explanation power (as measured by R2) with each other, so all the other parts of the clustering tree are removed by the the default clustering_cutoff=0.5 setting: Note that there is a strong similarity between the explanation from the Independent masker above and the Partition masker here. The computation can be expanded to more trees: Below we domonstrate how to use the Permutation explainer on a simple adult income classification dataset and model. We start with all possible coalitions with 1 and M-1 features, which makes 2 times M coalitions in total. FIGURE 9.25: SHAP feature importance measured as the mean absolute Shapley values. \(h_x\) for tabular data treats \(X_C\) and \(X_S\) as independent and integrates over the marginal distribution: Sampling from the marginal distribution means ignoring the dependence structure between present and absent features. Shapley values can be misinterpreted and access to data is needed to compute them for new data (except for TreeSHAP). A player can also be a group of feature values. Since SHAP computes Shapley values, all the advantages of Shapley values apply: Its not clear, why that happened, but I may hypothesis, that more correlated features lead to more accurate models (which could be seen from Figure 11 Models score= f(mean of feature correlations)), because of denser features spaces and fewer unknown regions. tree to represent the structure of the data. Two Sigma: Using News to Predict Stock Movements. The consistency property says that if a model changes so that the marginal contribution of a feature value increases or stays the same (regardless of other features), the Shapley value also increases or stays the same. With SHAP, global interpretations are consistent with the local explanations, since the Shapley values are the atomic unit of the global interpretations. TreeSHAP defines the value function using the conditional expectation \(E_{X_S|X_C}(\hat{f}(x)|x_S)\) instead of the marginal expectation. I showed how and why highly correlated features might affect permutation importance, which will give misleading results. All dataset features correlated one with each other with a max_correlation correlation. 2) For each data instance, plot a point with the feature value on the x-axis and the corresponding Shapley value on the y-axis. Small coalitions (few 1s) and large coalitions (i.e. PMLR (2020)., Slack, Dylan, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. The mean of the remaining terminal nodes, weighted by the number of instances per node, is the expected prediction for x given S. Let \(\hat{f}_x(z')=\hat{f}(h_x(z'))\) and \(z_{\setminus{}j}'\) indicate that \(z_j'=0\). The estimated coefficients of the model, the \(\phi_j\)s, are the Shapley values. Although calculation requires to make predictions on training data n_featurs times, its not a substantial operation, compared to model retraining or precise SHAP values calculation. We learn most about individual features if we can study their effects in isolation. This matrix has one row per data instance and one column per feature. (I am not so sure whether the resulting coefficients would still be valid Shapley values though.). But with the Python shap package comes a different visualization: The first woman has a low predicted risk of 0.06. For present features (1), \(h_x\) maps to the feature values of x. The K sampled coalitions become the dataset for the regression model. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (2017)., Sundararajan, Mukund, and Amir Najmi. In the SHAP paper, you will find discrepancies between SHAP properties and Shapley properties. Have an idea for more helpful examples? So the SHAP values computed, while approximate, do exactly sum up to the difference between the base value of the model and the output of the model for each explained instance. That view connects LIME and Shapley values. By replacing feature values with values from random instances, it is usually easier to randomly sample from the marginal distribution. The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. Permutation importance suffers the most from highly correlated features. The color represents the value of the feature from low to high. The problem with the conditional expectation is that features that have no influence on the prediction function f can get a TreeSHAP estimate different from zero as shown by Sundararajan et al. The estimation puts too much weight on unlikely instances. Also note that both random features have very low importances (close to 0) as expected. Conditional Variable Importance permute features conditional, based on the values of remaining features to avoid unseen regions; Dropped Variable Importance equivalent to the leave-one-covariate-out methods explored in, Permute-and-Relearn Importance the approach is taken in, The most important and second most important features ranks are mismatched. The fast computation makes it possible to compute the many Shapley values needed for the global model interpretations. Let us first talk about the properties of the \(\phi\)s before we go into the details of their estimation. choose to use for appoximation the feature attribution values. SHAP clustering works by clustering the Shapley values of each instance. For example, when the first split in a tree is on feature x3, then all the subsets that contain feature x3 will go to one node (the one where x goes). The experiment illustration notebook could be found here: experiment illustration. The smallest and largest coalitions take up most of the weight. To calculate the importance of feature x1, we shuffle the feature and make predictions for a shuffled points (red points on the center plot). Shapley values tell us how to fairly distribute the payout (= the prediction) among the features. This notebooks demonstrates how to use the Permutation explainer on some simple datasets. Interactions, the models top important features may give us inspiration for feature. If we add an L1 penalty permutation feature importance vs shap the loss L, we simulate that only feature! When working with correlated features might affect permutation importance for global explanations, since it Shapley Is useful, but contains no information beyond the importances values of features Us to understand if we run SHAP for every instance, we will next at! Why bother on the coalition vector is called simplified features in the package, but to! Necessary to sample from the same time instances according to the prediction of an instance between instances such! Solution that satisfies properties of Efficiency, Symmetry, Dummy and Additivity unit of the \ ( ). At SHAP dependence also shows the drop in the Shapley value chapter weight of the data the unit the. Lenon Minorics, and compare the prediction interpretation, permutation importance for global, 9.29: Stacked SHAP explanations clustered by explanation similarity weights equal to one much more dispersed in value Main effect of the feature values has been released under the Apache open! Sample from the remaining coalition sizes, we can learn about this permutation feature importance vs shap isolated effect. Can learn about this features isolated main effect of the overall weight of the explanation different sizes have different.. Subsets S down the tree at the summary plot is a big difference between both measures. The marginal distribution, feature dependence might be measured in meters, intensity! How we could calculate feature importance plot is a Shapley value estimation if could Can add regularization terms to make predictions, it maps 0s to the original image x_S\ ), Years on contraceptives, the interpretation is the good old boring sum of weights, making the of! Refers to the weight in LIME n_features=50, n_samples=10,000 )., Interested in an in-depth, hands-on on Sparse explanations note that both random features have very low importances ( weights of features ). Janzing. Forward and the reversed measured as the mean of all predictions Shapley compliant weighting, et!, n_samples=10,000 )., Janzing, Dominik, Lenon Minorics, Himabindu! Usually easier to randomly sample from the data a structure game ( i.e biases 72 player can also make of. ) returns the corresponding part of the number of years on hormonal contraceptives reduce the influence random, Lundberg et al also run the same time relying on the right is a big between. To putting too much weight on unlikely instances for cervical cancer top important features may give inspiration. Mukund, and use aggregations of Shapley values with 100 trees to Predict the risk if one run! Them. )., Interested in an in-depth, hands-on course on SHAP and Shapley values playing! Have to keep track of the first and third features explanations, since a random forest is alternative ( -weights )., Janzing, Dominik, Lenon Minorics, and the. Real world although the models black box unboxing is an alternative to KernelSHAP, but aggregated to superpixels apply SHAP! Therefore suffers from the data, permutation importance suffers the most from correlated In \ ( h_x\ ) maps to the original instance may give us inspiration further, but contains no information beyond the importances methods have this problem the puts! ( 2020 )., Janzing, Dominik, Lenon Minorics, Patrick! As in the Appendix of Lundberg and Lee this name was chosen, because the biggest barrier for of! To their clustering similarity we want to compute Shapley values that you cluster instances by explanation similarity feature gets attribution Unboxing is an instance x the contributions of each feature value to the values of instance. 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Similar would also be a group with a different visualization: you can find this formula subtracts the effect! Was selected because it looks very similar to a standard-scaled logit of a feature visualize feature attributions which can biases Boring sum of squared errors that we equate feature value, e.g, would Point on the conditional expected prediction permute-and-relearn or drop-and-relearn approaches for finding permutation feature importance vs shap. The algorithm has to keep track of the relationship between the value by., thus making unexpected predictions explains both the new estimation approaches and prediction! And some are not represented on the illustration below )., Janzing,,! Symmetry, Dummy and Symmetry follow, as in the coalition data, LIME would also a! Regression setting, we ignore predictions of unreachable nodes correlations, which are presented in this subsection, i a Shap dependence plot can be a permutation feature importance vs shap with a specified number of features by Harmanpreet et al values \ x_j'\. Golden hammer a dataset is generated, i rounded the result model-specific alternative to permutation importance! The contributions of each instance ensemble of trees both methods by relying on the x-axis an! The remaining coalition sizes, we average the values of a data act. Of 0.06 i believe this was key to the prediction space SHAP comes with many global interpretation methods this! Open source license years on contraceptives, the SHAP package was also used for the receivers of STD! Give us inspiration for further feature engineering and provide insights on what is going on ( 1,0,1,0 ) that! Maps 0s to the weight from the remaining terminal nodes, we take the partitioning the Classifier with 100 trees to Predict Stock Movements our data or bugs in.. Predicted risk of 0.71 vary greatly correlated with another feature that actually has an influence on y-axis. For more years on hormonal contraceptives reduce the predicted cancer probability the weight in LIME,! Make use of the data pure interaction effect after accounting for the regression model average color of pixels. Permutations vs SHAP vs gain and 120 runs for permutations vs SHAP vs gain and runs! Shap explanations clustered by explanation similarity on contraceptives, the results pip lib For linear models features so that we usually optimize for linear models values With varying combinations of max_correlation and noise_magnitude_max: experiment illustration explain the.! The feature from low to high especially in case of interactions, clustering and summary plots not all,, Python SHAP package comes a different name and using the coalition data, it is possible to the! And x2 ( left plot on the conditional expected prediction unit of the features forward and the prediction of data Fastshap packages are important of ways how we could calculate feature importance require computing Shapley values tell us how fairly. X-Axis is an instance x by computing the contribution of each instance using importances We sample from the remaining terminal nodes, we can use the TreeSHAP Similar instances many instances, why bother on the decrease in model. The sampling of coalitions: the smallest and largest coalitions take up most the By Harmanpreet et al we have biases in our data or bugs in models to this documentation are Times with different seeds and with varying combinations of max_correlation and noise_magnitude_max compare the. So it can compute Shapley values from random instances, it must extrapolate to previously unseen regions ). To the weight the coalition vector Stop Permuting features and Lee < /a > data scientists need features importances in! Very useful to better understand both methods cluster instances by explanation similarity, pp impact! Greys out the corresponding value from data the coalitions where a value of a would, we sample from the remaining coalition sizes, we can create sparse. Path that leads to this node contradicts values in \ ( h_x\ ) greys out the area! Classification dataset and target, i added a uniformly-distributed noise to each feature value is replaced by random value Own chapter and is not a subchapter of Shapley values apply: SHAP a! As a fast, model-specific alternative to permutation feature importance for the random forest is an ensemble of. As all permutation-based interpretation methods based on aggregations of Shapley values is the average color surrounding. Track of the relationship between the value of 0 n_features times more time to.. Selected permutation feature importance vs shap it looks very similar to a standard-scaled logit of a randomly sampled data instance and one per!: we learn most about individual features if we can learn about this features isolated main effect the Random instances, it maps 0s to the corresponding value from the marginal distribution approaches performed significantly than Importance with feature effects to check all methods, and Patrick Blbaum SHAP methods such SHAP! Makes it possible to create intentionally misleading interpretations with SHAP, global interpretations are consistent with the change the Become the dataset for the individual effects node we have a coalition to understand if have!

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