‘precision_weighted’, ‘recall’, ‘recall_macro’, ‘recall_micro’, ‘recall_weighted’, ‘log_loss’, ‘pac_score’] Options for regression: ‘roc_auc’, ‘roc_auc_ovo_macro’, ‘average_precision’, ‘precision’, ‘precision_macro’, ‘precision_micro’, If eval_metric = None, it is automatically chosen based on problem_type.ĭefaults to ‘accuracy’ for binary and multiclass classification and ‘root_mean_squared_error’ for regression. This only impacts model.score(), as eval_metric is not used during training. Metric by which predictions will be ultimately evaluated on test data. If None, will attempt to infer the problem type based on training data labels during training. is this a binary/multiclass classification or regression problem (options: ‘binary’, ‘multiclass’, ‘regression’). If None, defaults to the model’s class name: self._class_._name_ problem_type str, default = None The final model directory will be path+name+os.p() Name of the subdirectory inside path where model will be saved. If None, a new unique time-stamped directory is chosen. AbstractModel ( path : str = None, name : str = None, problem_type : str = None, eval_metric : Union = None, hyperparameters = None ) ¶Ībstract model implementation from which all AutoGluon models inherit. PyTorch neural network models for classification/regression with tabular data.Ĭlass for neural network models that operate on tabular data.Ĭlass for fastai v1 neural network models that operate on tabular data.ĪbstractModel ¶ class. So this suffix was added to avoid overwriting the pre-existing model.Ībstract model implementation from which all AutoGluon models inherit. “_x”: Indicates that the name without this added suffix already existed in a different model, Validation scores of distilled models should only be compared against other distilled models. Via a call to TabularPredictor’s distill method. “_DSTL”: Indicates the model was created through model distillation Often, this model can outperform the original model because of using more data during training,īut is usually weaker if the original was a bagged ensemble (“_BAG”), but with much faster inference speed. Usually, there will be another model with the same name as this model minus the “_FULL” suffix. This model will have no validation score because all of the data (train and validation) was used as training data. “_FULL”: Indicates the model has been refit via TabularPredictor’s refit_full method.
TABULAR LIST HOW TO
Refer to “_FULL” for instructions on how to improve inference speed. This typically achieves a stronger result than any of the individual models alone,īut slows down inference speed significantly. “_BAG”: Indicates that the model is a bagged ensemble.Ī bagged ensemble contains multiple instances of the model (children) trained with different subsets of the data.ĭuring inference, these child models each predict on the data and their predictions are averaged in the final result. “/Tx”: Indicates that the model was trained via hyperparameter search (HPO). If a model lacks this suffix, then it is a base model and is at level 1 (“_L1”). “_Lx”: Indicates the stack level (x) the model is trained in, such as “_L1”, “_L2”, etc.Ī model with “_L1” suffix is a base model, meaning it does not depend on any other models. Models trained by TabularPredictor can have suffixes in their names that have special meanings. Deploying AutoGluon models with serverless templates.Deploying AutoGluon Models with AWS SageMaker.Text Prediction - Solving Multilingual Problems.Object Detection - Prepare Dataset for Object Detector.
Predicting Multiple Columns in a Table (Multi-Label Prediction).Multimodal Data Tables: Combining BERT/Transformers and Classical Tabular Models.Multimodal Data Tables: Tabular, Text, and Image.How to use AutoGluon for Kaggle competitions.Predicting Columns in a Table - In Depth.Predicting Columns in a Table - Quick Start.
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