- Fixing bug: --no-upload flag was not really used.
- Adding the --reports option to generate Gazibit reports.
- Adding the --shared flag to share the created dataset, model and evaluation.
- Fixing bug for model building, when objective field was specified and no --max-category was present the user given objective was not used.
- Fixing bug: max-category data stored even when --max-category was not used.
- Adding --missing-strategy option to allow different prediction strategies when a missing value is found in a split field. Available for local predictions, batch predictions and evaluations.
- Adding new --delete options: --newer-than and --older-than to delete lists of resources according to their creation date.
- Adding --multi-dataset flag to generate a new dataset from a list of equally structured datasets.
- Bug fixing: resume from multi-label processing from dataset was not working.
- Bug fixing: max parallel resource creation check did not check that all the
- older tasks ended, only the last of the slot. This caused more tasks than permitted to be sent in parallel.
- Improving multi-label training data uploads by zipping the extended file and transforming booleans from True/False to 1/0.
- Bug fixing: dataset objective field is not updated each time --objective is used, but only if it differs from the existing objective.
- Storing the --max-categories info (its number and the chosen other label) in user_metadata.
- Fix when using the combined method in --max-categories models. The combination function now uses confidence to choose the predicted category.
- Allowing full content text fields to be also used as --max-categories objective fields.
- Fix solving objective issues when its column number is zero.
- Adding the --objective-weights option to point to a CSV file containing the weights assigned to each class.
- Adding the --label-aggregates option to create new aggregate fields on the multi label fields such as count, first or last.
- Fix in local random forests' predictions. Sometimes the fields used in all the models were not correctly retrieved and some predictions could be erroneus.
- Fix to allow the input data for multi-label predictions to be expanded.
- Fix to retrieve from the models definition info the labels that were given by the user in its creation in multi-label models.
- Adding new --balance option to automatically balance all the classes evenly.
- Adding new --weight-field option to use the field contents as weights for the instances.
- Adding new --source-attributes, --ensemble-attributes, --evaluation-attributes and --batch-prediction-attributes options.
- Refactoring --multi-label resources to include its related info in the user_metadata attribute.
- Refactoring the main routine.
- Adding --batch-prediction-tag for delete operations.
- Fix to transmit --training-separator when creating remote sources.
- Fix for multiple multi-label fields: headers did not match rows contents in some cases.
- Fix for datasets generated using the --new-fields option. The new dataset was not used in model generation.
- Adding --multi-label-fields to provide a comma-separated list of multi-label fields in a file.
- Fix for ensembles' local predictions when order is used in tie break.
- Fix for duplicated model ids in models file.
- Adding new --node-threshold option to allow node limit in models.
- Adding new --model-attributes option pointing to a JSON file containing model attributes for model creation.
- Fix for missing modules during installation.
- Adding the --max-categories option to handle datasets with a high number of categories.
- Adding the --method combine option to produce predictions with the sets of datasets generated using --max-categories option.
- Fixing problem with --max-categories when the categorical field is not a preferred field of the dataset.
- Changing the --datasets option behaviour: it points to a file where dataset ids are stored, one per line, and now it reads all of them to be used in model and ensemble creation.
- Adding confidence to predictions output in full format
- Bug fixing: multi-label predictions failed when the --ensembles option is used to provide the ensemble information
- Bug fixing: --dataset-price could not be set.
- Adding the threshold combination method to the local ensemble.
- Bug fixing: --model-fields option with absolute field names was not compatible with multi-label classification models.
- Changing resource type checking function.
- Bug fixing: evaluations did not use the given combination method.
- Bug fixing: evaluation of an ensemble had turned into evaluations of its
- models.
- Adding pruning to the ensemble creation configuration options
- Changing fields_map column order: previously mapped dataset column number to model column number, now maps model column number to dataset column number.
- Adding evaluations to multi-label models.
- Bug fixing: unicode characters greater than ascii-127 caused crash in multi-label classification
- Adapting to predictions issued by the high performance prediction server and the 0.9.0 version of the python bindings.
- Support for shared models using the same version on python bindings.
- Support for different server names using environment variables.
- Adding ensembles' predictions for multi-label objective fields
- Bug fixing: in evaluation mode, evaluation for --dataset and --number-of-models > 1 did not select the 20% hold out instances to test the generated ensemble.
- Adding text analysis through the corresponding bindings
- Adding support for multi-label objective fields
- Adding --prediction-headers and --prediction-fields to improve --prediction-info formatting options for the predictions file
- Adding the ability to read --test input data from stdin
- Adding --seed option to generate different splits from a dataset
- Adding --test-separator flag
- Bug fixing: resume crash when remote predictions were not completed
- Bug fixing: Fields object for input data dict building lacked fields
- Bug fixing: test data was repeated in remote prediction function
- Bug fixing: Adding replacement=True as default for ensembles' creation
- Adding --max-parallel-evaluations flag
- Bug fixing: matching seeds in models and evaluations for cross validation
- Changing --model-fields and --dataset-fields flag to allow adding/removing fields with +/- prefix
- Refactoring local and remote prediction functions
- Adding 'full data' option to the --prediction-info flag to join test input data with prediction results in predictions file
- Fixing errors in documentation and adding install for windows info
- Adding new flag to control predictions file information
- Bug fixing: using default sample-rate in ensemble evaluations
- Adding standard deviation to evaluation measures in cross-validation
- Bug fixing: using only-model argument to download fields in models
- Adding delete for ensembles
- Creating ensembles when the number of models is greater than one
- Remote predictions using ensembles
- Adding cross-validation feature
- Using user locale to create new resources in BigML
- Adding --ensemble flag to use ensembles in predictions and evaluations
- Deep refactoring of main resources management
- Fixing bug in batch_predict for no headers test sets
- Fixing bug for wide dataset's models than need query-string to retrieve all fields
- Fixing bug in test asserts to catch subprocess raise
- Adding default missing tokens to models
- Adding stdin input for --train flag
- Fixing bug when reading descriptions in --field-attributes
- Refactoring to get status from api function
- Adding confidence to combined predictions
- Evaluations management
- console monitoring of process advance
- resume option
- user defaults
- Refactoring to improve readability
- Improved locale management.
- Adds progressive handling for large numbers of models.
- More options in field attributes update feature.
- New flag to combine local existing predictions.
- More methods in local predictions: plurality, confidence weighted.
- New flag for locale settings configuration.
- Filtering only finished resources.
- Fix to ensure windows compatibility.
- Initial release.