FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner.
FLAML has a .NET implementation as well from ML.NET Model Builder in Visual Studio 2022. This ML.NET blog describes the improvement brought by FLAML.
FLAML requires Python version >= 3.6. It can be installed from pip:
pip install flaml
To run the notebook examples
,
install flaml with the [notebook] option:
pip install flaml[notebook]
from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification")
automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
from flaml import tune
tune.run(evaluation_function, config={}, low_cost_partial_config={}, time_budget_s=3600)
from flaml.default import LGBMRegressor
# Use LGBMRegressor in the same way as you use lightgbm.LGBMRegressor.
estimator = LGBMRegressor()
# The hyperparameters are automatically set according to the training data.
estimator.fit(X_train, y_train)
You can find a detailed documentation about FLAML here where you can find the API documentation, use cases and examples.
In addition, you can find:
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