AutoML Basics Cheat Sheet
Automate model selection, hyperparameter tuning, and pipeline search with AutoML libraries like Auto-sklearn, FLAML, and AutoGluon.
2 PagesBeginnerMar 8, 2026
FLAML Quickstart
Fit an AutoML model that searches algorithms and hyperparameters within a time budget.
python
from flaml import AutoMLautoml = AutoML()automl.fit( X_train, y_train, task="classification", time_budget=120, # seconds metric="roc_auc",)print("Best estimator:", automl.best_estimator)print("Best config:", automl.best_config)preds = automl.predict(X_test)
AutoGluon Tabular Predictor
Train and ensemble multiple model families on a tabular dataset with one call.
python
from autogluon.tabular import TabularPredictorpredictor = TabularPredictor(label="target", eval_metric="f1").fit( train_data=train_df, time_limit=600, presets="best_quality",)leaderboard = predictor.leaderboard(test_df, silent=True)predictions = predictor.predict(test_df)
Auto-sklearn Classifier
Run a Bayesian-optimization-driven search over sklearn pipelines with meta-learning warm starts.
python
import autosklearn.classificationclf = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=300, per_run_time_limit=30, ensemble_size=10,)clf.fit(X_train, y_train)print(clf.leaderboard())y_pred = clf.predict(X_test)
When to Use Which Tool
Quick guidance for picking an AutoML library based on constraints.
- FLAML- fastest, lightweight, good default for tight time budgets
- AutoGluon- best raw accuracy via stacked ensembles, heavier compute cost
- Auto-sklearn- strong meta-learning warm starts, sklearn-native pipelines
- H2O AutoML- scales well on Spark/large datasets, good leaderboard tooling
- time_budget / time_left_for_this_task- wall-clock cap on the whole search, not per model
Pro Tip
Treat AutoML output as a strong baseline, not a final model — always inspect the leaderboard's top 3 candidates manually, since the single 'best' model by validation score is sometimes the most overfit one.
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