AutoML Modes

Built-in modes

There are 3 built-in modes available in AutoML:

• Explain - to be used when the user wants to explain and understand the data.
• Perform - to be used when the user wants to train a model that will be used in real-life use cases.
• Compete - To be used for machine learning competitions (maximum performance!).

		AutoML Modes
	Explain	Perform	Compete
		Algorithms
Baseline
Linear
Decision Tree
Random Forest
Extra Trees
XGBoost
LightGBM
CatBoost
Neural Network
Nearest Neighbors
Ensemble
Stacking
		Steps
simple_algorithms
default_algorithms
not_so_random
golden_features
insert_random_feature
feature_selection
hill_climbing_1
hill_climbing_2
ensemble
stack
ensemble_stacked
		Validation
	75%/25% train/test split	5-fold CV, Shuffle, Stratify	10-fold CV, Shuffle, Stratify
		Explanations
	explain_level=2	explain_level=1	explain_level=0
Learning curves
Importance plots
SHAP plots
		Tuning
Parameters	start_random_models=1, hill_climbing_steps=0, top_models_to_improve=0	start_random_models=5, hill_climbing_steps=2, top_models_to_improve=2	start_random_models=10, hill_climbing_steps=2, top_models_to_improve=3
Models with default hyperparemeters	1	1	1
Models with not_so_random hyperparemeters	0	4	9
hill_climbing steps	0	2	2
Top models imporoved in each hill_climbing step	0	2	3
Total models tuned for each algorithm1	1	about 132	about 222

Custom modes

User can define his own modes by setting the parameters in AutoML constructor (AutoML API).

Example setting:

automl = AutoML(
    algorithms=["CatBoost", "Xgboost", "LightGBM"],
    model_time_limit=30*60,
    start_random_models=10,
    hill_climbing_steps=3,
    top_models_to_improve=3,
    golden_features=True,
    features_selection=False,
    stack_models=True,
    train_ensemble=True,
    explain_level=0,
    validation_strategy={
        "validation_type": "kfold",
        "k_folds": 4,
        "shuffle": False,
        "stratify": True,
    }
)

• It will train models with CatBoost, Xgboost and LightGBM algorithms.
• Each model will be trained for 30 minutes (30*60 seconds). total_time_limit is not set.
• There will be trained about 10+3*3*2=28 unstacked models and 10 stacked models for each algorithm. (There is stacked up to 10 models for each algorithm)
• There will trained Ensemble based on unstacked models and Ensemble_Stacked from unstacked and stacked models.
• In total there will be about 3*28+2=86 models trained.
• explain_level=0 means that there will be only learning curves saved. No other explanations will be computed.

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1. Not every algorithm is tuned. Models which are not tuned: Baseline, Decision Tree, Linear, Nearest Neighbors. ↩

2. The exact number cannot be given, because sometimes the newly generated hyperparameters are rejected during not_so_random or hill_climbing steps because of model duplicates or invalid hyperparameters set. ↩↩