Simple API
- train_model ( data , target_to_predict , task , metric = 'auto' , time_budget = None , test_data = None )
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Return a fully-trained model that has been selected and tuned for optimal performance on your data and task.
- Parameters :
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data ( str or pd.DataFrame ) –
- The input data. It can be one of the following options:
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String - a CSV file path
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Pandas DataFrame - a table
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target_to_predict ( str ) – The name of the column in your data that is the target for the model to predict
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task ( "classification" , "regression" or Task object ) – desired task
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metric ( str , default="auto" ) – The name of the scoring metric that AutoML uses when selecting and tuning your model. If “auto”, an appropriate metric will be automatically chosen for your data and task.
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time_budget ( float or None , default=None ) – The maximum time budget in seconds. if None, there is no maximum time budget.
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test_data ( str or pd.DataFrame or None , default=None ) –
Data that is used to estimate the quality of the trained model on data that it will evaluate in the future. It can be one of the following options:
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String, a CSV file path
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Pandas DataFrame, a table
if None, the test scores are estimated automatically by reserving 20% of the training data for evaluation of the final model.
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- Returns :
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A fully-trained model that is ready to predict the target on new data.
- Return type :
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Classifier or Regressor
- evaluate_model_quality ( model , data )
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Evaluate model quality
- Parameters :
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model ( Classifier , Regressor ) – The model to evaluate
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data ( str , or pd.DataFrame ) –
The data on which to evaluate the model. It can be one of the following options:
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String - a CSV file path
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Pandas DataFrame - a table
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- Returns :
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score of the model on the provided data
- Return type :
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pd.DataFrame