AutoML 实战自动机器学习工具对比与使用1. AutoML 工具对比AutoML 工具 ├── Auto-sklearn基于 scikit-learn自动选择算法和超参 ├── H2O AutoML企业级支持分布式 ├── TPOT基于遗传算法的流水线优化 ├── AutoKeras深度学习 AutoML └── FLAML微软轻量快速2. Auto-sklearnfromautosklearn.classificationimportAutoSklearnClassifier automlAutoSklearnClassifier(time_left_for_this_task300,# 总时间 5 分钟per_run_time_limit30,# 单次 30 秒n_jobs-1)automl.fit(X_train,y_train)print(automl.leaderboard())y_predautoml.predict(X_test)3. H2O AutoMLimporth2ofromh2o.automlimportH2OAutoML h2o.init()trainh2o.H2OFrame(pd.concat([X_train,y_train],axis1))testh2o.H2OFrame(X_test)amlH2OAutoML(max_runtime_secs300,max_models20,seed42)aml.train(ytarget_column,training_frametrain)print(aml.leaderboard())predsaml.leader.predict(test)4. FLAMLfromflamlimportAutoML automlAutoML()automl.fit(X_train,y_train,taskclassification,time_budget300,metricaccuracy,estimator_list[lgbm,xgboost,rf,extra_tree])print(f最佳模型:{automl.best_estimator})print(f最佳参数:{automl.best_config})print(f最佳分数:{automl.best_loss:.4f})总结工具优势适用场景Auto-sklearnscikit-learn 生态表格数据H2O分布式/企业级大规模数据FLAML轻量/快速快速原型