AutoML#

../_images/automl.png

Use DataRobot Autopilot to train challenger models on a dataset and establish a champion

Prediction and deployment methods execute on the present champion model at the time of calling. Training is performed within a new, automatically created DataRobot project.

DataRobot provides a large number of optional configuration settings that influence the Autopilot process. See the drx DRConfig class and it’s attributes to explore available options.

Usage#

Train#

import pandas as pd
import datarobotx as drx

df = pd.read_csv('https://s3.amazonaws.com/datarobot_public_datasets/10K_2007_to_2011_Lending_Club_Loans_v2_mod_80.csv')

model = drx.AutoMLModel()
model.fit(df, target='is_bad')

Train with additional configuration#

model_2 = drx.AutoMLModel(name="My project name")
config = model_2.get_params()
config.Modeling.AutoML.blend_best_models = False
model_2.set_params(**config)

model_2.fit(df, target='is_bad')

See also

AutoMLModel can also be used with OTV (time-aware partitioning) - see this workflow for more information.

Predict#

test_df = pd.read_csv('https://s3.amazonaws.com/datarobot_public_datasets/10K_2007_to_2011_Lending_Club_Loans_v2_mod_20.csv')
predictions = model.predict(test_df)
class_probs = model.predict_proba(test_df)

Deploy#

deployment = model.deploy()

API Reference#

`AutoMLModel`([name])	AutoML orchestrator.
`DRConfig`([Data, Target, Featurization, ...])	DataRobot configuration.
`Deployment`([deployment_id])	DataRobot ML Ops deployment.