Explainability Dashboard

If you want a complete interactive dashboard, just run the main function with the relevant inputs. With this dashboard, you can get all the explainability techniques under one roof.

Main Function

explainx.ai(X_Data, Y_Data, model, model_name, mode)

The main function requires 4 main arguments and 1 display argument.

X_Data: Pandas DataFrame containing the dataset without the y_variable/predicting variable. The data set needs to be a CSV file.

Y_Data: Pandas Series containing the y_variable/predicting variable data only.

model: This is your model training function. It can be Model.train or Model.fit (Training Function)

model_name: Name of your model. We support multiple models and you can print out a list of supported model names by calling the explainx.models() function e.g. 'xgboost', 'catboost'

mode: By default, the application provides you a link that opens up in a new tab. If you want to use the interface within the Jupyter notebook, add mode="inline"