Wave ML provides a simple, high-level API for training, deploying, scoring and explaining machine learning models, letting you build predictive and decision-support applications entirely in Python.
You can find the project in a separate repository here.
Wave ML is work in progress and it's not ready for a production use.
To use the package, simply import
Wave ML provides four high-level functions:
build_model(): Train a model on a dataset, given the column to be predicted.
Model.predict(): Make a prediction.
save_model(): Save your model.
load_model(): Load your previously saved model.
build_model() to train a model. The function accepts a dataset and a target column (the column to be predicted):
The call to
build_model() automatically determines if the prediction task is classification (predict a category or class) or regression (predict a real value, often a quantity).
Once the model is built, we can get the model's predictions using its
You can aso get the model's predictions by directly passing in the test rows:
To save this model locally, use
To load a saved model, use