State of the art to explain any blackbox Machine Learning model.
Project description
explainX
explainX.ai helps data scientists understand, explain and validate any machine learning model - in just one line of code. Checkout explainx.ai to learn more.
Use the package manager pip to install foobar.
pip install explainx``
## Usage
#Import the library
from explainx import *
#Load Dataset
X_data, Y_data = explainx.dataset_boston()
#Pass X_data, Y_data as numpy arrays into your XGBoost Model
model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=Y_data), 100)
#Pass your X_data, Y_data, y_variable name, model and model name to the explainx function
explainx.ai(X_data, Y_data, model, model_name="xgboost")
#Click on the link to access the dashboard
App running on https://127.0.0.1:8050
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
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