Mli configuration¶
mli_sample_above_for_scoring¶
mli_sample_above_for_scoring (Number)
Default value 1000000
When number of rows are above this limit sample for MLI for scoring UI data.
mli_sample_above_for_training¶
mli_sample_above_for_training (Number)
Default value 100000
When number of rows are above this limit sample for MLI for training surrogate models.
mli_interpreter_status_cache_size¶
mli_interpreter_status_cache_size (Number)
Default value 1000
Maximum number of interpreters status cache entries.
mli_sample_training¶
mli_sample_training (Boolean)
Default value True
not only sample training, but also sample scoring.
mli_strict_version_check¶
mli_strict_version_check (Boolean)
Default value True
Strict version check for MLI
mli_cloud_name¶
mli_cloud_name (String)
Default value ''
MLI cloud name
mli_ice_per_bin_strategy¶
mli_ice_per_bin_strategy (Boolean)
Default value False
Compute original model ICE using per feature’s bin predictions (true) or use 《one frame》 strategy (false).
mli_dia_default_max_cardinality¶
mli_dia_default_max_cardinality (Number)
Default value 10
By default DIA will run for categorical columns with cardinality <= mli_dia_default_max_cardinality.
mli_dia_default_min_cardinality¶
mli_dia_default_min_cardinality (Number)
Default value 2
By default DIA will run for categorical columns with cardinality >= mli_dia_default_min_cardinality.
enable_mli_keeper¶
enable_mli_keeper (Boolean)
Default value True
Enable MLI keeper which ensures efficient use of filesystem/memory/DB by MLI.
enable_mli_sa¶
enable_mli_sa (Boolean)
Default value True
Enable MLI Sensitivity Analysis
enable_mli_priority_queues¶
enable_mli_priority_queues (Boolean)
Default value True
Enable priority queues based explainers execution. Priority queues restrict available system resources and prevent system over-utilization. Interpretation execution time might be (significantly) slower.
mli_sequential_task_execution¶
mli_sequential_task_execution (Boolean)
Default value True
Explainers are run sequentially by default. This option can be used to run all explainers in parallel which can - depending on hardware strength and the number of explainers - decrease interpretation duration. Consider explainer dependencies, random explainers order and hardware over utilization.
mli_dia_sample_size¶
Sample size for Disparate Impact Analysis (Number)
Default value 100000
When number of rows are above this limit, then sample for Disparate Impact Analysis.
mli_pd_sample_size¶
Sample size for Partial Dependence Plot. (Number)
Default value 25000
When number of rows are above this limit, then sample for Partial Dependence Plot.
new_mli_list_only_explainable_datasets¶
new_mli_list_only_explainable_datasets (Boolean)
Default value False
In New Interpretation screen show only datasets which can be used to explain a selected model. This can slow down the server significantly.
enable_mli_async_api¶
enable_mli_async_api (Boolean)
Default value True
Enable async/await-based non-blocking MLI API
enable_mli_sa_main_chart_aggregator¶
enable_mli_sa_main_chart_aggregator (Boolean)
Default value True
Enable main chart aggregator in Sensitivity Analysis
mli_sa_sampling_limit¶
Sample size for SA (Number)
Default value 500000
When to sample for Sensitivity Analysis (number of rows after sampling).
mli_sa_main_chart_aggregator_limit¶
mli_sa_main_chart_aggregator_limit (Number)
Default value 1000
Run main chart aggregator in Sensitivity Analysis when the number of dataset instances is bigger than given limit.
mli_predict_safe¶
mli_predict_safe (Boolean)
Default value False
Use predict_safe() (true) or predict_base() (false) in MLI (PD, ICE, SA, …).
mli_max_surrogate_retries¶
mli_max_surrogate_retries (Number)
Default value 5
Number of max retries should the surrogate model fail to build.
enable_mli_symlinks¶
enable_mli_symlinks (Boolean)
Default value True
Allow use of symlinks (instead of file copy) by MLI explainer procedures.
h2o_mli_fraction_memory¶
h2o_mli_fraction_memory (Float)
Default value 0.45
Fraction of memory to allocate for h2o MLI jar
excluded_mli_explainers¶
Exclude specific explainers by explainer ID (List)
Default value []
To exclude e.g. Sensitivity Analysis explainer use: excluded_mli_explainers=[〈h2oaicore.mli.byor.recipes.sa_explainer.SaExplainer〉].
enable_ws_perfmon¶
enable_ws_perfmon (Boolean)
Default value False
Enable RPC API performance monitor.
mli_kernel_explainer_workers¶
mli_kernel_explainer_workers (Number)
Default value 4
Number of parallel workers when scoring using MOJO in Kernel Explainer.
mli_nlp_tokenizer¶
mli_nlp_tokenizer (String)
Default value 'tfidf'
Tokenizer used to extract tokens from text columns for MLI.
mli_image_enable¶
mli_image_enable (Boolean)
Default value True
Enable MLI for image experiments.
mli_max_explain_rows¶
The maximum number of rows allowed to get the local explanation result. (Number)
Default value 500
The maximum number of rows allowed to get the local explanation result, increase the value may jeopardize overall performance, change the value only if necessary.
mli_nlp_max_tokens_rows¶
The maximum number of rows allowed to get the NLP token importance result. (Number)
Default value 50
The maximum number of rows allowed to get the NLP token importance result, increasing the value may consume too much memory and negatively impact the performance, change the value only if necessary.
mli_nlp_min_parallel_rows¶
The minimum number of rows to enable parallel execution for NLP local explanations calculation. (Number)
Default value 10
The minimum number of rows to enable parallel execution for NLP local explanations calculation.
mli_run_legacy_defaults¶
Run legacy defaults in addition to current default explainers in MLI. (Boolean)
Default value False
Run legacy defaults in addition to current default explainers in MLI.
mli_run_explainers_sequentially¶
Run explainers sequentially for one given MLI job. (Boolean)
Default value False
Run explainers sequentially for one given MLI job.