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# you can use one of the following four interpretable models as a global surrogate to the black box modelįrom import LGBMExplainableModelįrom import LinearExplainableModelįrom import SGDExplainableModelįrom import DecisionTreeExplainableModel
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# "features" and "classes" fields are optionalįeatures=breast_cancer_data.feature_names,įrom import MimicExplainer
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The following code blocks show how to instantiate an explainer object with TabularExplainer, MimicExplainer, and PFIExplainer locally. To make your explanations and visualizations more informative, you can choose to pass in feature names and output class names if doing classification.To initialize an explainer object, pass your model and some training data to the explainer's constructor.X_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data,Ĭlf = svm.SVC(gamma=0.001, C=100., probability=True) # load breast cancer dataset, a well-known small dataset that comes with scikit-learnįrom sklearn.datasets import load_breast_cancerįrom sklearn.model_selection import train_test_splitīreast_cancer_data = load_breast_cancer()Ĭlasses = breast_cancer_data.target_names.tolist() Train a sample model in a local Jupyter Notebook. The following example shows how to use the interpretability package on your personal machine without contacting Azure services. Generate feature importance value on your personal machine Use a visualization dashboard to interact with your model explanations, both in a Jupyter Notebook and in the Azure Machine Learning studio.ĭeploy a scoring explainer alongside your model to observe explanations during inferencing.įor more information on the supported interpretability techniques and machine learning models, see Model interpretability in Azure Machine Learning and sample notebooks.įor guidance on how to enable interpretability for models trained with automated machine learning see, Interpretability: model explanations for automated machine learning models (preview). Upload explanations to Azure Machine Learning Run History. In this how-to guide, you learn to use the interpretability package of the Azure Machine Learning Python SDK to perform the following tasks:Įxplain the entire model behavior or individual predictions on your personal machine locally.Įnable interpretability techniques for engineered features.Įxplain the behavior for the entire model and individual predictions in Azure.
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