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.