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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