AutoML

분류:인공지능 Auto Machine Learning; 자동 기계학습; 자동 머신러닝

AutoML은 기계학습상의 HITL을 개선하여 완전한 자동화를 추구하는 인공지능 분야 및 그 기술을 아우르는 용어이다.

AutoML의 의의

• 현재 기계학습의 상당 부분은 인간의 수작업에 의존
  • 데이터 전처리, 하이퍼 파라미터 최적화 등
• 이런 수작업을 줄이거나 자동화할 경우 기계학습 응용성 극대화 가능

관련 도구 및 라이브러리

• Autogluon

예시

autogluon을 이용하여 가작 적합한 모델을 찾는 과정

AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
	To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 400, Val Rows: 100
Fitting 13 L1 models ...
Fitting model: KNeighborsUnif ...
	0.73	 = Validation score   (accuracy)
	0.0s	 = Training   runtime
	0.01s	 = Validation runtime
Fitting model: KNeighborsDist ...
	0.65	 = Validation score   (accuracy)
	0.0s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting model: LightGBMXT ...
	0.83	 = Validation score   (accuracy)
	1.01s	 = Training   runtime
	0.01s	 = Validation runtime
Fitting model: LightGBM ...
	0.85	 = Validation score   (accuracy)
	0.23s	 = Training   runtime
	0.01s	 = Validation runtime
Fitting model: RandomForestGini ...
	0.84	 = Validation score   (accuracy)
	0.58s	 = Training   runtime
	0.06s	 = Validation runtime
Fitting model: RandomForestEntr ...
	0.83	 = Validation score   (accuracy)
	0.47s	 = Training   runtime
	0.06s	 = Validation runtime
Fitting model: CatBoost ...
	0.85	 = Validation score   (accuracy)
	1.13s	 = Training   runtime
	0.01s	 = Validation runtime
Fitting model: ExtraTreesGini ...
	0.82	 = Validation score   (accuracy)
	0.47s	 = Training   runtime
	0.06s	 = Validation runtime
Fitting model: ExtraTreesEntr ...
	0.81	 = Validation score   (accuracy)
	0.47s	 = Training   runtime
	0.06s	 = Validation runtime
Fitting model: NeuralNetFastAI ...
	0.82	 = Validation score   (accuracy)
	3.2s	 = Training   runtime
	0.02s	 = Validation runtime
Fitting model: XGBoost ...
	0.87	 = Validation score   (accuracy)
	0.23s	 = Training   runtime
	0.01s	 = Validation runtime
Fitting model: NeuralNetTorch ...
	0.85	 = Validation score   (accuracy)
	2.08s	 = Training   runtime
	0.01s	 = Validation runtime
Fitting model: LightGBMLarge ...
	0.83	 = Validation score   (accuracy)
	0.35s	 = Training   runtime
	0.01s	 = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
	0.87	 = Validation score   (accuracy)
	0.38s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 11.19s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20221125_121604/")