Models configuration

enable_constant_model

Constant models (String) (Expert Setting)

Default value 'auto'

Whether to enable constant models (‘auto’/’on’/’off’)

enable_decision_tree

Decision Tree models (String) (Expert Setting)

Default value 'auto'

Whether to enable Decision Tree models (‘auto’/’on’/’off’). ‘auto’ disables decision tree unless only non-constant model chosen.

enable_glm

GLM models (String) (Expert Setting)

Default value 'auto'

Whether to enable GLM models (‘auto’/’on’/’off’)

enable_xgboost_gbm

XGBoost GBM models (String) (Expert Setting)

Default value 'auto'

Whether to enable XGBoost GBM models (‘auto’/’on’/’off’)

enable_lightgbm

LightGBM models (String) (Expert Setting)

Default value 'auto'

Whether to enable LightGBM models (‘auto’/’on’/’off’)

enable_tensorflow

TensorFlow models (String) (Expert Setting)

Default value 'auto'

Whether to enable TensorFlow models (‘auto’/’on’/’off’)

enable_grownet

PyTorch GrowNet models (String) (Expert Setting)

Default value 'auto'

Whether to enable PyTorch-based GrowNet models (‘auto’/’on’/’off’)

enable_ftrl

FTRL models (String) (Expert Setting)

Default value 'auto'

Whether to enable FTRL support (follow the regularized leader) model (‘auto’/’on’/’off’)

enable_rulefit

RuleFit models (String) (Expert Setting)

Default value 'auto'

Whether to enable RuleFit support (beta version, no mojo) (‘auto’/’on’/’off’)

enable_zero_inflated_models

Zero-Inflated models (String) (Expert Setting)

Default value 'auto'

Whether to enable automatic addition of zero-inflated models for regression problems with zero-inflated target values that meet certain conditions: y >= 0, y.std() > y.mean()

enable_xgboost_rf

Enable XGBoost RF mode (String) (Expert Setting)

Default value 'auto'

Whether to enable XGBoost RF mode without early stopping.

Disabled unless switched on.

enable_xgboost_gbm_dask

Enable dask_cudf (multi-GPU) XGBoost GBM/RF (String) (Expert Setting)

Default value 'auto'

Whether to enable dask_cudf (multi-GPU) version of XGBoost GBM/RF.

Disabled unless switched on. Only applicable for single final model without early stopping. No Shapley possible.

enable_lightgbm_dask

Enable dask (multi-node) LightGBM (String) (Expert Setting)

Default value 'auto'

Whether to enable multi-node LightGBM.

Disabled unless switched on.

hyperopt_shift_leak

Whether to do hyperopt for leakage/shift (Boolean) (Expert Setting)

Default value False

If num_inner_hyperopt_trials_prefinal > 0,

then whether to do hyper parameter tuning during leakage/shift detection. Might be useful to find non-trivial leakage/shift, but usually not necessary.

hyperopt_shift_leak_per_column

Whether to do hyperopt for leakage/shift for each column (Boolean) (Expert Setting)

Default value False

If num_inner_hyperopt_trials_prefinal > 0,

then whether to do hyper parameter tuning during leakage/shift detection, when checking each column.

num_inner_hyperopt_trials_prefinal

Number of trials for hyperparameter optimization during model tuning only (Number) (Expert Setting)

Default value 0

Number of trials for Optuna hyperparameter optimization for tuning and evolution models.

0 means no trials. For small data, 100 is ok choice, while for larger data smaller values are reasonable if need results quickly. If using RAPIDS or DASK, hyperparameter optimization keeps data on GPU entire time. Currently applies to XGBoost GBM/Dart and LightGBM. Useful when there is high overhead of DAI outside inner model fit/predict, so this tunes without that overhead. However, can overfit on a single fold when doing tuning or evolution, and if using CV then averaging the fold hyperparameters can lead to unexpected results.

num_inner_hyperopt_trials_final

Number of trials for hyperparameter optimization for final model only (Number) (Expert Setting)

Default value 0

Number of trials for Optuna hyperparameter optimization for final models.

0 means no trials. For small data, 100 is ok choice, while for larger data smaller values are reasonable if need results quickly. Applies to final model only even if num_inner_hyperopt_trials=0. If using RAPIDS or DASK, hyperparameter optimization keeps data on GPU entire time. Currently applies to XGBoost GBM/Dart and LightGBM. Useful when there is high overhead of DAI outside inner model fit/predict, so this tunes without that overhead. However, for final model each fold is independently optimized and can overfit on each fold, after which predictions are averaged (so no issue with averaging hyperparameters when doing CV with tuning or evolution).

num_hyperopt_individuals_final

Number of individuals in final ensemble to use Optuna on (Number) (Expert Setting)

Default value -1

Number of individuals in final model (all folds/repeats for given base model) to optimize with Optuna hyperparameter tuning.

-1 means all. 0 is same as choosing no Optuna trials. Might be only beneficial to optimize hyperparameters of best individual (i.e. value of 1) in ensemble.

optuna_pruner

Optuna Pruners (String) (Expert Setting)

Default value 'MedianPruner'

Optuna Pruner to use (applicable to XGBoost and LightGBM that support Optuna callbacks). To disable choose None.

optuna_sampler

Optuna Samplers (String) (Expert Setting)

Default value 'TPESampler'

Optuna Pruner to use (applicable to XGBoost and LightGBM that support Optuna callbacks).

enable_xgboost_hyperopt_callback

Enable Optuna XGBoost Pruning callback (Boolean) (Expert Setting)

Default value True

Whether to enable Optuna’s XGBoost Pruning callback to abort unpromising runs. Not done if tuning learning rate.

enable_lightgbm_hyperopt_callback

Enable Optuna LightGBM Pruning callback (Boolean) (Expert Setting)

Default value True

Whether to enable Optuna’s LightGBM Pruning callback to abort unpromising runs. Not done if tuning learning rate.

enable_xgboost_dart

XGBoost Dart models (String) (Expert Setting)

Default value 'auto'

Whether to enable XGBoost Dart models (‘auto’/’on’/’off’)

enable_xgboost_dart_dask

Enable dask_cudf (multi-GPU) XGBoost Dart (String) (Expert Setting)

Default value 'auto'

Whether to enable dask_cudf (multi-GPU) version of XGBoost Dart.

Disabled unless switched on. If have only 1 GPU, then only uses dask_cudf if use_dask_for_1_gpu is True

Only applicable for single final model without early stopping. No Shapley possible.

enable_xgboost_rf_dask

Enable dask_cudf (multi-GPU) XGBoost RF (String) (Expert Setting)

Default value 'auto'

Whether to enable dask_cudf (multi-GPU) version of XGBoost RF.

Disabled unless switched on. If have only 1 GPU, then only uses dask_cudf if use_dask_for_1_gpu is True

Only applicable for single final model without early stopping. No Shapley possible.

enable_lightgbm_boosting_types

LightGBM Boosting types (List) (Expert Setting)

Default value ['gbdt']

Which boosting types to enable for LightGBM (gbdt = boosted trees, rf_early_stopping = random forest with early stopping rf = random forest (no early stopping), dart = drop-out boosted trees with no early stopping

enable_lightgbm_multiclass_balancing

LightGBM multiclass balancing (String) (Expert Setting)

Default value 'auto'

Whether to enable automatic class weighting for imbalanced multiclass problems. Can make worse probabilities, but improve confusion-matrix based scorers for rare classes without the need to manually calibrate probabilities or fine-tune the label creation process.

enable_lightgbm_cat_support

LightGBM categorical support (Boolean) (Expert Setting)

Default value False

Whether to enable LightGBM categorical feature support (runs in CPU mode even if GPUs enabled, and no MOJO built)

enable_lightgbm_linear_tree

LightGBM linear_tree mode (Boolean) (Expert Setting)

Default value False

Whether to enable LightGBM linear_tree handling (only CPU mode currently, no L1 regularization – mae objective, and no MOJO build).

enable_lightgbm_extra_trees

LightGBM extra trees mode (Boolean) (Expert Setting)

Default value False

Whether to enable LightGBM extra trees mode to help avoid overfitting

lightgbm_monotone_constraints_method

Method to use for monotonicity constraints for LightGBM (String) (Expert Setting)

Default value 'intermediate'

basic: as fast as when no constraints applied, but over-constrains the predictions. intermediate: very slightly slower, but much less constraining while still holding monotonicity and should be more accurate than basic. advanced: slower, but even more accurate than intermediate.

lightgbm_monotone_penalty

LightGBM Monotone Penalty (Float) (Expert Setting)

Default value 0.0

Forbids any monotone splits on the first x (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter. https://lightgbm.readthedocs.io/en/latest/Parameters.html#monotone_penalty

enable_lightgbm_cuda_support

LightGBM CUDA support (Boolean) (Expert Setting)

Default value False

Whether to enable LightGBM CUDA implementation instead of OpenCL.

CUDA with LightGBM only supported for Pascal+ (compute capability >=6.0)

show_constant_model

Whether to show constant models in iteration panel even when not best model (Boolean) (Expert Setting)

Default value False

Whether to show constant models in iteration panel even when not best model.

xgboost_reg_objectives

Select XGBoost regression objectives. (List) (Expert Setting)

Default value ['reg:squarederror']

Select objectives allowed for XGBoost.

Added to allowed mutations (the default reg:squarederror is in sample list 3 times) Note: logistic, tweedie, gamma, poisson are only valid for targets with positive values. Note: The objective relates to the form of the (regularized) loss function,

used to determine the split with maximum information gain, while the metric is the non-regularized metric

measured on the validation set (external or internally generated by DAI).

xgboost_reg_metrics

Select XGBoost regression metrics. (List) (Expert Setting)

Default value ['rmse', 'mae']

Select metrics allowed for XGBoost.

Added to allowed mutations (the default rmse and mae are in sample list twice). Note: tweedie, gamma, poisson are only valid for targets with positive values.

xgboost_binary_metrics

Select XGBoost binary metrics. (List) (Expert Setting)

Default value ['logloss', 'auc', 'aucpr', 'error']

Select which objectives allowed for XGBoost.

Added to allowed mutations (all evenly sampled).

lightgbm_reg_objectives

Select LightGBM regression objectives. (List) (Expert Setting)

Default value ['mse', 'mae']

Select objectives allowed for LightGBM.

Added to allowed mutations (the default mse is in sample list 2 times if selected). “binary” refers to logistic regression. Note: If choose quantile/huber or fair and data is not normalized, recommendation is to use params_lightgbm to specify reasonable value of alpha (for quantile or huber) or fairc (for fair) to LightGBM. Note: mse is same as rmse correponding to L2 loss. mae is L1 loss. Note: tweedie, gamma, poisson are only valid for targets with positive values. Note: The objective relates to the form of the (regularized) loss function,

used to determine the split with maximum information gain, while the metric is the non-regularized metric

measured on the validation set (external or internally generated by DAI).

lightgbm_reg_metrics

Select LightGBM regression metrics. (List) (Expert Setting)

Default value ['rmse', 'mse', 'mae']

Select metrics allowed for LightGBM.

Added to allowed mutations (the default rmse is in sample list three times if selected). Note: If choose huber or fair and data is not normalized, recommendation is to use params_lightgbm to specify reasonable value of alpha (for huber or quantile) or fairc (for fair) to LightGBM. Note: tweedie, gamma, poisson are only valid for targets with positive values.

lightgbm_binary_objectives

Select LightGBM binary objectives. (List) (Expert Setting)

Default value ['binary', 'xentropy']

Select objectives allowed for LightGBM.

Added to allowed mutations (the default binary is in sample list 2 times if selected)

lightgbm_binary_metrics

Select LightGBM binary metrics. (List) (Expert Setting)

Default value ['binary', 'binary', 'auc']

Select which binary metrics allowed for LightGBM.

Added to allowed mutations (all evenly sampled).

lightgbm_multi_metrics

Select LightGBM multiclass metrics. (List) (Expert Setting)

Default value ['multiclass', 'multi_error']

Select which metrics allowed for multiclass LightGBM.

Added to allowed mutations (evenly sampled if selected).

tweedie_variance_power_list

tweedie_variance_power parameters (List) (Expert Setting)

Default value [1.5, 1.2, 1.9]

tweedie_variance_power parameters to try for XGBoostModel and LightGBMModel if tweedie is used.

First value is default.

huber_alpha_list

huber parameters (List) (Expert Setting)

Default value [0.9, 0.3, 0.5, 0.6, 0.7, 0.8, 0.1, 0.99]

huber parameters to try for LightGBMModel if huber is used.

First value is default.

fair_c_list

fair c parameters (List) (Expert Setting)

Default value [1.0, 0.1, 0.5, 0.9]

fair c parameters to try for LightGBMModel if fair is used.

First value is default.

poisson_max_delta_step_list

poisson_max_delta_step parameters (List) (Expert Setting)

Default value [0.7, 0.9, 0.5, 0.2]

poisson max_delta_step parameters to try for LightGBMModel if poisson is used.

First value is default.

quantile_alpha

quantile alpha parameters (List) (Expert Setting)

Default value [0.9, 0.95, 0.99, 0.6]

quantile alpha parameters to try for LightGBMModel if quantile is used.

First value is default.

reg_lambda_glm_default

default reg_lambda regularization parameter (Float) (Expert Setting)

Default value 0.0004

Default reg_lambda regularization for GLM.

params_lightgbm

params_lightgbm (Dict) (Expert Setting)

Default value {}

Parameters for LightGBM to override DAI parameters e.g. 'eval_metric' instead of 'metric' should be used e.g. params_lightgbm="{'objective': 'binary', 'n_estimators': 100, 'max_leaves': 64, 'random_state': 1234}" e.g. params_lightgbm="{'n_estimators': 600, 'learning_rate': 0.1, 'reg_alpha': 0.0, 'reg_lambda': 0.5, 'gamma': 0, 'max_depth': 0, 'max_bin': 128, 'max_leaves': 256, 'scale_pos_weight': 1.0, 'max_delta_step': 3.469919910597877, 'min_child_weight': 1, 'subsample': 0.9, 'colsample_bytree': 0.3, 'tree_method': 'gpu_hist', 'grow_policy': 'lossguide', 'min_data_in_bin': 3, 'min_child_samples': 5, 'early_stopping_rounds': 20, 'num_classes': 2, 'objective': 'binary', 'eval_metric': 'binary', 'random_state': 987654, 'early_stopping_threshold': 0.01, 'monotonicity_constraints': False, 'silent': True, 'debug_verbose': 0, 'subsample_freq': 1}" avoid including “system”-level parameters like 'n_gpus': 1, 'gpu_id': 0, , 'n_jobs': 1, 'booster': 'lightgbm' also likely should avoid parameters like: ‘objective’: ‘binary’, unless one really knows what one is doing (e.g. alternative objectives) See: https://xgboost.readthedocs.io/en/latest/parameter.html And see: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst Can also pass objective parameters if choose (or in case automatically chosen) certain objectives https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters

params_xgboost

params_xgboost (Dict) (Expert Setting)

Default value {}

Parameters for XGBoost to override DAI parameters similar parameters as LightGBM since LightGBM parameters are transcribed from XGBoost equivalent versions e.g. params_xgboost="{'n_estimators': 100, 'max_leaves': 64, 'max_depth': 0, 'random_state': 1234}" See: https://xgboost.readthedocs.io/en/latest/parameter.html

params_xgboost_rf

params_xgboost_rf (Dict) (Expert Setting)

Default value {}

Like params_xgboost but for XGBoost random forest.

params_dart

params_dart (Dict) (Expert Setting)

Default value {}

Like params_xgboost but for XGBoost’s dart method

params_tensorflow

Parameters for TensorFlow (Dict) (Expert Setting)

Default value {}

Parameters for TensorFlow to override DAI parameters e.g. params_tensorflow="{'lr': 0.01, 'add_wide': False, 'add_attention': True, 'epochs': 30, 'layers': (100, 100), 'activation': 'selu', 'batch_size': 64, 'chunk_size': 1000, 'dropout': 0.3, 'strategy': '1cycle', 'l1': 0.0, 'l2': 0.0, 'ort_loss': 0.5, 'ort_loss_tau': 0.01, 'normalize_type': 'streaming'}" See: https://keras.io/ , e.g. for activations: https://keras.io/activations/ Example layers: (500, 500, 500), (100, 100, 100), (100, 100), (50, 50) Strategies: '1cycle' or 'one_shot', See: https://github.com/fastai/fastai ‘one_shot” is not allowed for ensembles. normalize_type: ‘streaming’ or ‘global’ (using sklearn StandardScaler)

params_gblinear

params_gblinear (Dict) (Expert Setting)

Default value {}

Parameters for XGBoost’s gblinear to override DAI parameters e.g. params_gblinear="{'n_estimators': 100}" See: https://xgboost.readthedocs.io/en/latest/parameter.html

params_decision_tree

params_decision_tree (Dict) (Expert Setting)

Default value {}

Parameters for Decision Tree to override DAI parameters parameters should be given as XGBoost equivalent unless unique LightGBM parameter e.g. 'eval_metric' instead of 'metric' should be used e.g. params_decision_tree="{'objective': 'binary', 'n_estimators': 100, 'max_leaves': 64, 'random_state': 1234}" e.g. params_decision_tree="{'n_estimators': 1, 'learning_rate': 1, 'reg_alpha': 0.0, 'reg_lambda': 0.5, 'gamma': 0, 'max_depth': 0, 'max_bin': 128, 'max_leaves': 256, 'scale_pos_weight': 1.0, 'max_delta_step': 3.469919910597877, 'min_child_weight': 1, 'subsample': 0.9, 'colsample_bytree': 0.3, 'tree_method': 'gpu_hist', 'grow_policy': 'lossguide', 'min_data_in_bin': 3, 'min_child_samples': 5, 'early_stopping_rounds': 20, 'num_classes': 2, 'objective': 'binary', 'eval_metric': 'logloss', 'random_state': 987654, 'early_stopping_threshold': 0.01, 'monotonicity_constraints': False, 'silent': True, 'debug_verbose': 0, 'subsample_freq': 1}" avoid including “system”-level parameters like 'n_gpus': 1, 'gpu_id': 0, , 'n_jobs': 1, 'booster': 'lightgbm' also likely should avoid parameters like: 'objective': 'binary:logistic', unless one really knows what one is doing (e.g. alternative objectives) See: https://xgboost.readthedocs.io/en/latest/parameter.html And see: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst Can also pass objective parameters if choose (or in case automatically chosen) certain objectives https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters

params_rulefit

params_rulefit (Dict) (Expert Setting)

Default value {}

Parameters for Rulefit to override DAI parameters e.g. params_rulefit="{'max_leaves': 64}" See: https://xgboost.readthedocs.io/en/latest/parameter.html

params_ftrl

params_ftrl (Dict) (Expert Setting)

Default value {}

Parameters for FTRL to override DAI parameters

params_grownet

params_grownet (Dict) (Expert Setting)

Default value {}

Parameters for GrowNet to override DAI parameters

params_tune_mode

Mode to handle params_tune_ tomls (String) (Expert Setting)

Default value 'override_and_first_as_default'

How to handle tomls like params_tune_lightgbm. override: For any key in the params_tune_ toml dict, use the list of values instead of DAI’s list of values. override_and_first_as_default: like override, but also use first entry in tuple/list (if present) as override as replacement for (e.g.) params_lightgbm when using params_tune_lightgbm. exclusive: Only tune the keys in the params_tune_ toml dict, unless no keys are present. Otherwise use DAI’s default values. exclusive_and_first_as_default: Like exclusive but same first as default behavior as override_and_first_as_default. In order to fully control hyperparameter tuning, either one should set “override” mode and include every hyperparameter and at least one value in each list within the dictionary, or choose “exclusive” and then rely upon DAI unchanging default values for any keys not given. For custom recipes, one can use recipe_dict to pass hyperparameters and if using the “get_one()” function in a custom recipe, and if user_tune passed contains the hyperparameter dictionary equivalent of params_tune_ tomls, then this params_tune_mode will also work for custom recipes.

params_final_auto_adjust

Adjust trees/LR (Boolean) (Expert Setting)

Default value True

Whether to adjust GBM trees, learning rate, and early_stopping_rounds for GBM models or recipes with _is_gbm=True. True: auto mode, that changes trees/LR/stopping if tune_learning_rate=false and early stopping is supported by the model and model is GBM or from custom individual with parameter in adjusted_params. False: disable any adjusting from tuning-evolution into final model. Setting this to false is required if (e.g.) one changes params_lightgbm or params_tune_lightgbm and wanted to preserve the tuning-evolution values into the final model. One should also set tune_learning_rate to true to tune the learning_rate, else it will be fixed to some single value.

params_tune_lightgbm

params_tune_lightgbm (Dict) (Expert Setting)

Default value {}

Dictionary of key:lists of values to use for LightGBM tuning, overrides DAI’s choice per key e.g. params_tune_lightgbm="{'min_child_samples': [1,2,5,100,1000], 'min_data_in_bin': [1,2,3,10,100,1000]}"

params_tune_xgboost

params_tune_xgboost (Dict) (Expert Setting)

Default value {}

Like params_tune_lightgbm but for XGBoost e.g. params_tune_xgboost="{'max_leaves': [8, 16, 32, 64]}"

params_tune_xgboost_rf

params_tune_xgboost_rf (Dict) (Expert Setting)

Default value {}

Like params_tune_lightgbm but for XGBoost random forest e.g. params_tune_xgboost_rf="{'max_leaves': [8, 16, 32, 64]}"

params_tune_decision_tree

params_tune_decision_tree (Dict) (Expert Setting)

Default value {}

Dictionary of key:lists of values to use for LightGBM Decision Tree tuning, overrides DAI’s choice per key e.g. params_tune_decision_tree="{'min_child_samples': [1,2,5,100,1000], 'min_data_in_bin': [1,2,3,10,100,1000]}"

params_tune_dart

params_tune_dart (Dict) (Expert Setting)

Default value {}

Like params_tune_lightgbm but for XGBoost’s Dart e.g. params_tune_dart="{'max_leaves': [8, 16, 32, 64]}"

params_tune_tensorflow

params_tune_tensorflow (Dict) (Expert Setting)

Default value {}

Like params_tune_lightgbm but for TensorFlow e.g. params_tune_tensorflow="{'layers': [(10,10,10), (10, 10, 10, 10)]}"

params_tune_gblinear

params_tune_gblinear (Dict) (Expert Setting)

Default value {}

Like params_tune_lightgbm but for gblinear e.g. params_tune_gblinear="{'reg_lambda': [.01, .001, .0001, .0002]}"

params_tune_rulefit

params_tune_rulefit (Dict) (Expert Setting)

Default value {}

Like params_tune_lightgbm but for rulefit e.g. params_tune_rulefit="{'max_depth': [4, 5, 6]}"

params_tune_ftrl

params_tune_ftrl (Dict) (Expert Setting)

Default value {}

Like params_tune_lightgbm but for ftrl

params_tune_grownet

params_tune_grownet (Dict) (Expert Setting)

Default value {}

Like params_tune_lightgbm but for GrowNet e.g. params_tune_grownet="{'input_dropout': [0.2, 0.5]}"

max_nestimators

Max. number of trees/iterations (Number) (Expert Setting)

Default value 3000

Maximum number of GBM trees or GLM iterations. Can be reduced for lower accuracy and/or higher interpretability. Early-stopping usually chooses less. Ignored if fixed_max_nestimators is > 0.

fixed_max_nestimators

Fixed max. number of trees/iterations (-1 = auto mode) (Number) (Expert Setting)

Default value -1

Fixed maximum number of GBM trees or GLM iterations. If > 0, ignores max_nestimators and disables automatic reduction due to lower accuracy or higher interpretability. Early-stopping usually chooses less.

n_estimators_list_no_early_stopping

n_estimators list to sample from for model mutations for models that do not use early stopping (List) (Expert Setting)

Default value [50, 100, 150, 200, 250, 300]

LightGBM dart mode and normal rf mode do not use early stopping, and they will sample from these values for n_estimators. XGBoost Dart mode will also sample from these n_estimators. Also applies to XGBoost Dask models that do not yet support early stopping or callbacks. For default parameters it chooses first value in list, while mutations sample from the list.

min_learning_rate_final

Minimum learning rate for final ensemble GBM models (Float) (Expert Setting)

Default value 0.01

Lower limit on learning rate for final ensemble GBM models. In some cases, the maximum number of trees/iterations is insufficient for the final learning rate, which can lead to no early stopping triggered and poor final model performance. Then, one can try increasing the learning rate by raising this minimum, or one can try increasing the maximum number of trees/iterations.

max_learning_rate_final

Maximum learning rate for final ensemble GBM models (Float) (Expert Setting)

Default value 0.05

Upper limit on learning rate for final ensemble GBM models

max_nestimators_feature_evolution_factor

Reduction factor for max. number of trees/iterations during feature evolution (Float) (Expert Setting)

Default value 0.2

factor by which max_nestimators is reduced for tuning and feature evolution

min_learning_rate

Min. learning rate for feature engineering GBM models (Float) (Expert Setting)

Default value 0.05

Lower limit on learning rate for feature engineering GBM models

max_learning_rate

Max. learning rate for feature engineering GBM models (Float) (Expert Setting)

Default value 0.5

Upper limit on learning rate for GBM models If want to override min_learning_rate and min_learning_rate_final, set this to smaller value

lock_ga_to_final_trees

Whether to lock tree parameters to final model values (Boolean) (Expert Setting)

Default value False

Whether to lock learning rate, tree count, early stopping rounds for GBM algorithms to the final model values.

tune_learning_rate

Whether to tune learning rate even for GBM algorithms with early stopping (Boolean) (Expert Setting)

Default value False

Whether to tune learning rate for GBM algorithms (if not doing just single final model). If tuning with Optuna, might help isolate optimal learning rate.

max_epochs

Max. number of epochs for TensorFlow / FTRL (Number) (Expert Setting)

Default value 50

Max. number of epochs for TensorFlow and FTRL models

max_max_depth

Max. tree depth (and Max. max_leaves as 2**max_max_depth) (Number) (Expert Setting)

Default value 12

Maximum tree depth (and corresponding max max_leaves as 2**max_max_depth)

max_max_bin

Max. max_bin for tree features (Number) (Expert Setting)

Default value 256

Maximum max_bin for tree features

rulefit_max_num_rules

Max. number of rules for RuleFit (-1 for all) (Number) (Expert Setting)

Default value -1

Max number of rules to be used for RuleFit models (-1 for all)

rulefit_max_tree_depth

rulefit_max_tree_depth (Number) (Expert Setting)

Default value 6

Max tree depth for RuleFit models

rulefit_max_num_trees

rulefit_max_num_trees (Number) (Expert Setting)

Default value 500

Max number of trees for RuleFit models

fixed_ensemble_level

Ensemble level for final modeling pipeline (Number) (Expert Setting)

Default value -1

Fixed ensemble_level -1 = auto, based upon ensemble_accuracy_switch, accuracy, size of data, etc. 0 = No ensemble, only final single model on validated iteration/tree count 1 = 1 model, multiple ensemble folds (cross-validation) >=2 = >=2 models, multiple ensemble folds (cross-validation)

cross_validate_single_final_model

Cross-validate single final model (Boolean) (Expert Setting)

Default value True

If enabled, use cross-validation to determine optimal parameters for single final model,

and to be able to create training holdout predictions.

ensemble_meta_learner

Type of ensemble meta learner. Blender is recommended for most use cases. (String) (Expert Setting)

Default value 'blender'

Model to combine base model predictions, for experiments that create a final pipeline consisting of multiple base models.

blender: Creates a linear blend with non-negative weights that add to 1 (blending) - recommended extra_trees: Creates a tree model to non-linearly combine the base models (stacking) - experimental, and recommended to also set enable cross_validate_meta_learner. neural_net: Creates a neural net model to non-linearly combine the base models (stacking) - experimental, and recommended to also set enable cross_validate_meta_learner.

cross_validate_meta_learner

Cross-validate meta learner for final ensemble. (Boolean) (Expert Setting)

Default value False

If enabled, use cross-validation to create an ensemble for the meta learner itself. Especially recommended for ensemble_meta_learner='extra_trees', to make unbiased training holdout predictions. Will disable MOJO if enabled. Not needed for ensemble_meta_learner='blender'.”

parameter_tuning_num_models

Number of models during tuning phase (-1 = auto) (Number) (Expert Setting)

Default value -1

Number of models to tune during pre-evolution phase Can make this lower to avoid excessive tuning, or make higher to do enhanced tuning. -1 : auto

parameter_tuning_num_models_sequence

Number of default simple models during tuning phase (-1 = auto) (Number) (Expert Setting)

Default value -1

Number of models (out of all parameter_tuning_num_models) to have as SEQUENCE instead of random features/parameters. -1 : auto, use at least one default individual per model class tuned

parameter_tuning_num_models_extra

Number of extra models during tuning phase (-1 = auto) (Number) (Expert Setting)

Default value -1

Number of models to add during tuning that cover other cases, like for TS having no TE on time column groups. -1 : auto, adds additional models to protect against overfit on high-gain training features.

num_tuning_instances

Num. in tuning (Dict) (Expert Setting)

Default value {}

Dictionary of model class name (keys) and number (values) of instances.

imbalance_sampling_method

Sampling method for imbalanced binary classification problems (String) (Expert Setting)

Default value 'off'

Sampling method for imbalanced binary classification problems. Choices are: “auto”: sample both classes as needed, depending on data “over_under_sampling”: over-sample the minority class and under-sample the majority class, depending on data “under_sampling”: under-sample the majority class to reach class balance “off”: do not perform any sampling

imbalance_sampling_threshold_min_rows_original

Threshold for minimum number of rows in original training data to allow imbalanced sampling techniques. For smaller data, will disable imbalanced sampling, no matter what imbalance_sampling_method is set to. (Number) (Expert Setting)

Default value 100000

For smaller data, there’s no generally no benefit in using imbalanced sampling methods.

imbalance_ratio_sampling_threshold

Ratio of majority to minority class for imbalanced binary classification to trigger special sampling techniques if enabled (Number) (Expert Setting)

Default value 5

For imbalanced binary classification: ratio of majority to minority class equal and above which to enable special imbalanced models with sampling techniques (specified by imbalance_sampling_method) to attempt to improve model performance.

heavy_imbalance_ratio_sampling_threshold

Ratio of majority to minority class for heavily imbalanced binary classification to only enable special sampling techniques if enabled (Number) (Expert Setting)

Default value 25

For heavily imbalanced binary classification: ratio of majority to minority class equal and above which to enable only special imbalanced models on full original data, without upfront sampling.

imbalance_sampling_number_of_bags

Number of bags for sampling methods for imbalanced binary classification (if enabled). -1 for automatic. (Number) (Expert Setting)

Default value -1

-1: automatic

imbalance_sampling_max_number_of_bags

Hard limit on number of bags for sampling methods for imbalanced binary classification. (Number) (Expert Setting)

Default value 10

-1: automatic

imbalance_sampling_max_number_of_bags_feature_evolution

Hard limit on number of bags for sampling methods for imbalanced binary classification during feature evolution phase. (Number) (Expert Setting)

Default value 3

Only for shift/leakage/tuning/feature evolution models. Not used for final models. Final models can

be limited by imbalance_sampling_max_number_of_bags.

imbalance_sampling_max_multiple_data_size

Max. size of data sampled during imbalanced sampling (in terms of dataset size) (Float) (Expert Setting)

Default value 1.0

Max. size of data sampled during imbalanced sampling (in terms of dataset size),

controls number of bags (approximately). Only for imbalance_sampling_number_of_bags == -1.

imbalance_sampling_target_minority_fraction

Target fraction of minority class after applying under/over-sampling techniques. -1.0 for automatic (Float) (Expert Setting)

Default value -1.0

A value of 0.5 means that models/algorithms will be presented a balanced target class distribution

after applying under/over-sampling techniques on the training data. Sometimes it makes sense to choose a smaller value like 0.1 or 0.01 when starting from an extremely imbalanced original target distribution. -1.0: automatic

ftrl_max_interaction_terms_per_degree

Max. number of automatic FTRL interactions terms for 2nd, 3rd, 4th order interactions terms (each) (Number) (Expert Setting)

Default value 10000

Samples the number of automatic FTRL interactions terms to no more than this value (for each of 2nd, 3rd, 4th order terms)

enable_bootstrap

Whether to enable bootstrap sampling for validation and test scores. (Boolean) (Expert Setting)

Default value True

Whether to enable bootstrap sampling. Provides error bars to validation and test scores based on the standard error of the bootstrap mean.

booster_for_fs_permute

booster_for_fs_permute (String) (Expert Setting)

Default value 'auto'

model_class_name_for_fs_permute

model_class_name_for_fs_permute (String) (Expert Setting)

Default value 'auto'

switch_from_tree_to_lgbm_if_can

switch_from_tree_to_lgbm_if_can (Boolean) (Expert Setting)

Default value True

default_booster

default_booster (String) (Expert Setting)

Default value 'lightgbm'

default_model_class_name

default_model_class_name (String) (Expert Setting)

Default value 'LightGBMModel'

tensorflow_added_num_classes_switch

Num. classes above which include Tensorflow (Number) (Expert Setting)

Default value 5

Number of classes above which to include TensorFlow (if TensorFlow is enabled),

even if not used exclusively. For small data this is decreased by tensorflow_num_classes_small_data_factor, and for bigger data, this is increased by tensorflow_num_classes_big_data_reduction_factor.

tensorflow_num_classes_switch

Num. classes above which to exclusively use TensorFlow (Number) (Expert Setting)

Default value 10

Number of classes above which to only use TensorFlow (if TensorFlow is enabled),

instead of others models set on ‘auto’ (models set to ‘on’ are still used). Up to tensorflow_num_classes_switch_but_keep_lightgbm, keep LightGBM. If small data, this is increased by tensorflow_num_classes_small_data_factor.

prediction_intervals

Compute prediction intervals (Boolean) (Expert Setting)

Default value True

Compute empirical prediction intervals (based on holdout predictions).

prediction_intervals_alpha

Confidence level for prediction intervals (Float) (Expert Setting)

Default value 0.9

Confidence level for prediction intervals.

pred_labels

Output labels for predictions created during the experiment for classification problems. (Boolean) (Expert Setting)

Default value True

Appends one extra output column with predicted target class (after the per-class probabilities).

Uses argmax for multiclass, and the threshold defined by the optimal scorer controlled by the ‘threshold_scorer’ expert setting for binary problems. This setting controls the training, validation and test set predictions (if applicable) that are created by the experiment. MOJO, scoring pipeline and client APIs control this behavior via their own version of this parameter.

max_abs_score_delta_train_valid

Max. absolute delta between training and validation scores for tree models. (Float) (Expert Setting)

Default value 0.0

Modify early stopping behavior for tree-based models (LightGBM, XGBoostGBM, CatBoost) such

that training score (on training data, not holdout) and validation score differ no more than this absolute value (i.e., stop adding trees once abs(train_score - valid_score) > max_abs_score_delta_train_valid). Keep in mind that the meaning of this value depends on the chosen scorer and the dataset (i.e., 0.01 for LogLoss is different than 0.01 for MSE). Experimental option, only for expert use to keep model complexity low. To disable, set to 0.0

max_rel_score_delta_train_valid

Max. relative delta between training and validation scores for tree models. (Float) (Expert Setting)

Default value 0.0

Modify early stopping behavior for tree-based models (LightGBM, XGBoostGBM, CatBoost) such

that training score (on training data, not holdout) and validation score differ no more than this relative value (i.e., stop adding trees once abs(train_score - valid_score) > max_rel_score_delta_train_valid * abs(train_score)). Keep in mind that the meaning of this value depends on the chosen scorer and the dataset (i.e., 0.01 for LogLoss is different than 0.01 for MSE). Experimental option, only for expert use to keep model complexity low. To disable, set to 0.0

glm_lambda_search

Do lambda search for GLM (String) (Expert Setting)

Default value 'auto'

Whether to search for optimal lambda for given alpha for XGBoost GLM.

If ‘auto’, disabled if training data has more rows * cols than final_pipeline_data_size or for multiclass experiments. Disabled always for ensemble_level = 0. Not always a good approach, can be slow for little payoff compared to grid search.

glm_lambda_search_by_eval_metric

Do lambda search for GLM by exact eval metric (Boolean) (Expert Setting)

Default value False

If XGBoost GLM lambda search is enabled, whether to do search by the eval metric (True)

or using the actual DAI scorer (False).

enable_early_stopping_threshold

Early stopping threshold (Float) (Expert Setting)

Default value -2.0

Whether to enable early stopping threshold for LightGBM, varying by accuracy.

Stops training once validation score changes by less than the threshold. This leads to fewer trees, usually avoiding wasteful trees, but may lower accuracy. However, it may also improve generalization by avoiding fine-tuning to validation set. 0 leads to value of 0 used, i.e. disabled > 0 means non-automatic mode using that relative value, scaled by first tree results of the metric for any metric. -1 means always enable, but the threshold itself is automatic (lower the accuracy, the larger the threshold). -2 means fully automatic mode, i.e. disabled unless reduce_mojo_size is true. In true, the lower the accuracy, the larger the threshold. NOTE: Automatic threshold is set so relative value of metric’s min_delta in LightGBM’s callback for early stopping is:

if accuracy <= 1:

early_stopping_threshold = 1e-1

elif accuracy <= 4:

early_stopping_threshold = 1e-2

elif accuracy <= 7:

early_stopping_threshold = 1e-3

elif accuracy <= 9:

early_stopping_threshold = 1e-4

else:

early_stopping_threshold = 0

glm_optimal_refit

glm_optimal_refit (Boolean)

Default value True

dump_modelparams_every_scored_indiv

Enable detailed scored model info (Boolean) (Expert Setting)

Default value True

Whether to dump every scored individual’s model parameters to csv/tabulated/json file produces files like: individual_scored.params.[txt, csv, json]. Each individual has a hash that matches the hash in the filenames produced if dump_varimp_every_scored_indiv=true, and the “unchanging hash” is the first parent hash (None if that individual is the first parent itself). These hashes can be used to track the history of the mutations.