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# Frequently Asked Questions

### About low_cost_partial_config in tune.​

• Definition and purpose: The low_cost_partial_config is a dictionary of subset of the hyperparameter coordinates whose value corresponds to a configuration with known low-cost (i.e., low computation cost for training the corresponding model). The concept of low/high-cost is meaningful in the case where a subset of the hyperparameters to tune directly affects the computation cost for training the model. For example, n_estimators and max_leaves are known to affect the training cost of tree-based learners. We call this subset of hyperparameters, cost-related hyperparameters. In such scenarios, if you are aware of low-cost configurations for the cost-related hyperparameters, you are recommended to set them as the low_cost_partial_config. Using the tree-based method example again, since we know that small n_estimators and max_leaves generally correspond to simpler models and thus lower cost, we set {'n_estimators': 4, 'max_leaves': 4} as the low_cost_partial_config by default (note that 4 is the lower bound of search space for these two hyperparameters), e.g., in LGBM. Configuring low_cost_partial_config helps the search algorithms make more cost-efficient choices. In AutoML, the low_cost_init_value in search_space() function for each estimator serves the same role.

• Usage in practice: It is recommended to configure it if there are cost-related hyperparameters in your tuning task and you happen to know the low-cost values for them, but it is not required (It is fine to leave it the default value, i.e., None).

• How does it work: low_cost_partial_config if configured, will be used as an initial point of the search. It also affects the search trajectory. For more details about how does it play a role in the search algorithms, please refer to the papers about the search algorithms used: Section 2 of Frugal Optimization for Cost-related Hyperparameters (CFO) and Section 3 of Economical Hyperparameter Optimization with Blended Search Strategy (BlendSearch).

### How does FLAML handle imbalanced data (unequal distribution of target classes in classification task)?​

Currently FLAML does several things for imbalanced data.

1. When a class contains fewer than 20 examples, we repeatedly add these examples to the training data until the count is at least 20.
2. We use stratified sampling when doing holdout and kf.
3. We make sure no class is empty in both training and holdout data.
4. We allow users to pass sample_weight to AutoML.fit().
5. User can customize the weight of each class by setting the custom_hp or fit_kwargs_by_estimator arguments. For example, the following code sets the weight for pos vs. neg as 2:1 for the RandomForest estimator:
from flaml import AutoMLfrom sklearn.datasets import load_irisX_train, y_train = load_iris(return_X_y=True)automl = AutoML()automl_settings = {    "time_budget": 2,    "task": "classification",    "log_file_name": "test/iris.log",    "estimator_list": ["rf", "xgboost"],}automl_settings["custom_hp"] = {    "xgboost": {        "scale_pos_weight": {            "domain": 0.5,            "init_value": 0.5,        }    },    "rf": {        "class_weight": {            "domain": "balanced",            "init_value": "balanced"        }    }}print(automl.model)

### How to interpret model performance? Is it possible for me to visualize feature importance, SHAP values, optimization history?​

You can use automl.model.estimator.feature_importances_ to get the feature_importances_ for the best model found by automl. See an example.

Packages such as azureml-interpret and sklearn.inspection.permutation_importance can be used on automl.model.estimator to explain the selected model. Model explanation is frequently asked and adding a native support may be a good feature. Suggestions/contributions are welcome.

Optimization history can be checked from the log. You can also retrieve the log and plot the learning curve.

### How to resolve out-of-memory error in AutoML.fit()​

• Set free_mem_ratio a float between 0 and 1. For example, 0.2 means try to keep free memory above 20% of total memory. Training may be early stopped for memory consumption reason when this is set.
• Set model_history False.
• If your data are already preprocessed, set skip_transform False. If you can preprocess the data before the fit starts, this setting can save memory needed for preprocessing in fit.
• If the OOM error only happens for some particular trials:
• set use_ray True. This will increase the overhead per trial but can keep the AutoML process running when a single trial fails due to OOM error.
• provide a more accurate size function for the memory bytes consumption of each config for the estimator causing this error.
• modify the search space for the estimators causing this error.
• or remove this estimator from the estimator_list.
• If the OOM error happens when ensembling, consider disabling ensemble, or use a cheaper ensemble option. (Example).