Recipes Settings

Include Specific Transformers

Select the transformer(s) that you want to use in the experiment. Use the Check All/Uncheck All button to quickly add or remove all transfomers at once. Note: If you uncheck all transformers so that none is selected, Driverless AI will ignore this and will use the default list of transformers for that experiment. This list of transformers will vary for each experiment.

The equivalent config.toml parameter is included_transformers.

Include Specific Preprocessing Transformers

Specify which transformers to use for preprocessing before other transformers are activated. Preprocessing transformers can take any original features and output arbitrary features that are used by the normal layer of transformers.

Notes:

  • Preprocessing transformers and all other layers of transformers are part of the Python and (if applicable) MOJO scoring packages.

  • Any custom transformer recipe or native DAI transformer can be used as a preprocessing transformer. For example, a preprocessing transformer can perform interactions, string concatenations, or date extractions as a preprocessing step before the next layer of Date and DateTime transformations are performed.

Caveats:

  1. one cannot currently do a time-series experiment on a time_column that hasn’t yet been made (setup of experiment only knows about original data, not transformed). However, one can use a run-time data recipe to (e.g.) convert a float date-time into string date-time, and this will be used by Driverless AIs Date and DateTime transformers as well as auto-detection of time series.

  2. in order to do a time series experiment with the GUI/client auto-selecting groups, periods, etc. the dataset must have time column and groups prepared ahead of experiment by user or via a one-time data recipe.

The equivalent config.toml parameter is included_pretransformers.

Number of Pipeline Layers

Specify the number of pipeline layers. This value defaults to 1. The equivalent config.toml parameter is num_pipeline_layers.

Note: This does not include the preprocessing layer specified by the Include Specific Preprocessing Transformers expert setting.

Include Specific Models

Specify the types of models that you want Driverless AI to build in the experiment. This list includes natively supported algorithms and models added with custom recipes.

Note: The ImbalancedLightGBM and ImbalancedXGBoostGBM models are closely tied with the Sampling Method for Imbalanced Binary Classification Problems option. Specifically:

Include Specific Scorers

Specify the scorer(s) that you want Driverless AI to include when running the experiment.

Scorer to Optimize Threshold to Be Used in Other Confusion-Matrix Based Scorers (For Binary Classification)

Specify the scorer used to optimize the binary probability threshold that is being used in related Confusion Matrix based scorers such as Precision, Recall, FalsePositiveRate, FalseDiscoveryRate, FalseOmissionRate, TrueNegativeRate, FalseNegativeRate, and NegativePredictiveValue. Select from the following:

  • Auto (Default): Use this option to sync the threshold scorer with the scorer used for the experiment. If this is not possible, F1 is used.

  • F05 More weight on precision, less weight on recall.

  • F1: Equal weight on precision and recall.

  • F2: Less weight on precision, more weight on recall.

  • MCC: Use this option when all classes are equally important.

Include Specific Data Recipes During Experiment

Specify whether to include specific data recipes during the experiment. Avoids need for separate data preparation step, builds data preparation within experiment and within python scoring package. But Mojo will require data preparation applied before making predictions.

The equivalent config.toml parameter is included_datas.

Probability to Add Transformers

Specify the unnormalized probability to add genes or instances of transformers with specific attributes. If no genes can be added, other mutations are attempted. This value defaults to 0.5.

Probability to Add Best Shared Transformers

Specify the unnormalized probability to add genes or instances of transformers with specific attributes that have shown to be beneficial to other individuals within the population. This value defaults to 0.5.

Probability to Prune Transformers

Specify the unnormalized probability to prune genes or instances of transformers with specific attributes. This value defaults to 0.5.

Probability to Mutate Model Parameters

Specify the unnormalized probability to change model hyper parameters. This value defaults to 0.25.

Probability to Prune Weak Features

Specify the unnormalized probability to prune features that have low variable importance instead of pruning entire instances of genes/transformers. This value defaults to 0.25.

Timeout in Minutes for Testing Acceptance of Each Recipe

Specify the number of minutes to wait until a recipe’s acceptance testing is aborted. A recipe is rejected if acceptance testing is enabled and it times out. This value defaults to 20.0.

Whether to Skip Failures of Transformers

Specify whether to avoid failed transformers. This is enabled by default.

Whether to Skip Failures of Models

Specify whether to avoid failed models. Failures are logged according to the specified level for logging skipped failures. This is enabled by default.

Level to Log for Skipped Failures

Specify one of the following levels for the verbosity of log failure messages for skipped transformers or models:

  • 0 = Log simple message

  • 1 = Log code line plus message (Default)

  • 2 = Log detailed stack traces