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@ -9,7 +9,7 @@ hyperparameter optimization and learner selection method invented by
Microsoft Research. Microsoft Research.
FLAML is easy to use: FLAML is easy to use:
1. With three lines of code, you can start using this economical and fast * With three lines of code, you can start using this economical and fast
AutoML engine as a scikit-learn style estimator. AutoML engine as a scikit-learn style estimator.
```python ```python
from flaml import AutoML from flaml import AutoML
@ -17,13 +17,13 @@ automl = AutoML()
automl.fit(X_train, y_train, task="classification") automl.fit(X_train, y_train, task="classification")
``` ```
2. You can restrict the learners and use FLAML as a fast hyperparameter tuning * You can restrict the learners and use FLAML as a fast hyperparameter tuning
tool for XGBoost, LightGBM, Random Forest etc. or a customized learner. tool for XGBoost, LightGBM, Random Forest etc. or a customized learner.
```python ```python
automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"]) automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
``` ```
3. You can embed FLAML in self-tuning software for just-in-time tuning with * You can embed FLAML in self-tuning software for just-in-time tuning with
low latency & resource consumption. low latency & resource consumption.
```python ```python
automl.fit(X_train, y_train, task="regression", time_budget=60) automl.fit(X_train, y_train, task="regression", time_budget=60)