Experiment Settings

This section includes settings that can be used to customize the experiment like total runtime, reproducibility level, pipeline building, feature brain control, adding config.toml settings and more.

max_runtime_minutes

max_runtime_minutes_until_abort

time_abort

pipeline-building-recipe

enable_genetic_algorithm

tournament_style

make_python_scoring_pipeline

make_mojo_scoring_pipeline

mojo_for_predictions

reduce_mojo_size

make_pipeline_visualization

benchmark_mojo_latency

mojo_building_timeout

mojo_building_parallelism

kaggle_username

kaggle_key

kaggle_timeout

min_num_rows

reproducibility_level

seed

allow_different_classes_across_fold_splits

save_validation_splits

max_num_classes

max_num_classes_compute_roc

max_num_classes_client_and_gui

roc_reduce_type

max_rows_cm_ga

use_feature_brain_new_experiments

feature_brain_level

feature_brain2

feature_brain3

feature_brain4

feature_brain5

force_model_restart_to_defaults

min_dai_iterations

target_transformer

fixed_num_folds_evolution

fixed_num_folds

fixed_only_first_fold_model

feature_evolution_data_size

final_pipeline_data_size

max_validation_to_training_size_ratio_for_final_ensemble

force_stratified_splits_for_imbalanced_threshold_binary

config_overrides

last_recipe

feature_brain_reset_score

feature_brain_save_every_iteration

which_iteration_brain

refit_same_best_individual

restart_refit_redo_origfs_shift_leak

brain_add_features_for_new_columns

force_model_restart_to_defaults

dump_modelparams_every_scored_indiv

fast_approx_num_trees

fast_approx_do_one_fold

fast_approx_do_one_model

fast_approx_contribs_num_trees

fast_approx_contribs_do_one_fold

fast_approx_contribs_do_one_model