Package: explainx
ExplainX.ai is a fast, scalable and end-to-end Explainable AI framework for data scientists & machine learning engineers.With explainX, you can understand overall model behavior, get the reasoning behind model predictions, remove biases and create convincing explanations for your business stakeholders.Why we need explainability & interpretibility?Essential for:Model debugging - Why did my model make a mistake? How can I improve the accuracy of the model?Detecting fairness issues - Is my model biased? If yes, where?Human-AI cooperation - How can I understand and trust the model's decisions?Regulatory compliance - Does my model satisfy legal & regulatory requirements?High-risk applications - Healthcare, Financial Services, FinTech, Judicial, Security etc,.Visit our website to learn more:https://www.explainx.aiTry it outInstalling explainXWorking ExamplesexplainX Dashboard FeaturesDocumentationHelp Us Improve explainX.aiInstallationPython 3.5+ | Linux, Mac, Windows (InstallMicrosoft C++ Build Toolsfirst to run on Windows.)pipinstallexplainxInstallation on the cloudIf you are using AWS SageMaker of Jupyter Notebook deployed on the cloud, visit our documentation for step-by-step guide installing and running explainX.Cloud Installation InstructionsExample UsageAfter successfully installing explainX, open up your Python IDE of Jupyter Notebook and simply follow the code below to use it:Import required module.fromexplainximport*fromsklearn.ensembleimportRandomForestClassifierfromsklearn.model_selectionimporttrain_test_splitLoad and split your dataset into x_data and y_data#Load Dataset: X_Data, Y_Data#X_Data = Pandas DataFrame#Y_Data = Numpy Array or ListX_data,Y_data=explainx.dataset_heloc()Split dataset into training & testing.X_train,X_test,Y_train,Y_test=train_test_split(X_data,Y_data,test_size=0.3,random_state=0)Train your model.# Train a RandomForest Modelmodel=RandomForestClassifier()model.fit(X_train,Y_train)Pass your model and dataset into the explainX function:explainx.ai(X_test,Y_test,model,model_name="randomforest")Click on the dashboard link to start exploring model behavior:Apprunningonhttps://0.0.0.0:8080If you are running explainX on the cloud e.g., AWS Sagemaker?https://0.0.0.0:8080will not work.
Please visit our documentation for installation instructions for the cloud:Cloud Installation InstructionsAfter installation is complete, just open yourterminaland run the following command.lt -h "https://serverless.social" -p [port number]lt -h "https://serverless.social" -p 8080Learn to analyze the dashboard by following this link:explainX Dashboard FeaturesVisit the documentation tolearn moreModels SupportedCatboostXGBoost==1.0.2Gradient Boosting RegressorRandomForest ModelSVMKNeighboursClassifierLogistic RegressionDecisionTreeClassifierScikit-learn ModelsNeural NetworksWalkthough Video TutorialPlease click on the image below to load the tutorial:(Note: Please manually set it to 720p or greater to have the text appear clearly)ContributingPull requests are welcome. In order to make changes to explainx, the ideal approach is to fork the repository then clone the fork locally.For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.Report IssuesPlease help us byreporting any issuesyou may have while using explainX.LicenseMIT