Tag: shap

SHAP Linear model waterfall with KernelExplainer and LinearExplainer

classification dataframe machine-learning python shap

I am working on binary classification and trying to explain my model using SHAP framework. I am using logistic regression algorithm. I would like to explain this model using both KernelExplainer and LinearExplainer. So, I tried the below code from SO here This threw an error as shown below AssertionError: Unknown type passed as data object: <class ‘shap.maskers._tabular.Independent’> How can

SHAP import local_accuracy

metrics python shap

I’m trying to test metrics from the shap library https://github.com/slundberg/shap/blob/master/shap/benchmark/metrics.py I tried calling metrics like this : But am always getting the error : Answer Try instead: Why? Inspecting package’s top level __init__.py you’ll find out the following commented line:

How to get SHAP values for each class on a multiclass classification problem in python

machine-learning python python-3.x shap xgboost

I have the following dataframe: For which I want to run a classification algorithm in order to predict the 3 classes So I split my dataset into train and test and I run an xgboost Now I would like to get the mean SHAP values for each class, instead of the mean from the absolute SHAP values generated from this

Why are shap values changing every time I call shap.plots.beeswarm?

python shap xgboost

So here’s my code using shap : Since I just plot three times the same shape values, I’d expect the three plots to be the same. However, it keeps on changing. After some research, it seems that a new value appear at the top at each call, but why ? Is it a bug in shap ? Edit 1 :

In Leave One Out Cross Validation, How can I Use `shap.Explainer()` Function to Explain a Machine Learning Model?

cross-validation leave-one-out python shap

Background of the Problem I want to explain the outcome of machine learning (ML) models using SHapley Additive exPlanations (SHAP) which is implemented in the shap library of Python. As a parameter of the function shap.Explainer(), I need to pass an ML model (e.g. XGBRegressor()). However, in each iteration of the Leave One Out Cross Validation (LOOCV), the ML model

Does SHAP in Python support Keras or TensorFlow models while using DeepExplainer?

deep-learning keras python shap tensorflow

I am currently using SHAP Package to determine the feature contributions. I have used the approach for XGBoost and RandomForest and it worked really well. Since the data I am working on is a sequential data I tried using LSTM and CNN to train the model and then get the feature importance using the SHAP’s DeepExplainer; but it is continuously