create(dataset, target, features=None, validation_set='auto', verbose=True)¶
Automatically create a suitable classifier model based on the provided training data.
To use specific options of a desired model, use the
createfunction of the corresponding model.
- dataset : SFrame
Dataset for training the model.
- target : string
Name of the column containing the target variable. The values in this column must be of string or integer type. String target variables are automatically mapped to integers in the order in which they are provided. For example, a target variable with ‘cat’ and ‘dog’ as possible values is mapped to 0 and 1 respectively with 0 being the base class and 1 being the reference class. Use model.classes to retrieve the order in which the classes are mapped.
- features : list[string], optional
Names of the columns containing features. ‘None’ (the default) indicates that all columns except the target variable should be used as features.
The features are columns in the input SFrame that can be of the following types:
- Numeric: values of numeric type integer or float.
- Categorical: values of type string.
- Array: list of numeric (integer or float) values. Each list element is treated as a separate feature in the model.
- Dictionary: key-value pairs with numeric (integer or float) values Each key of a dictionary is treated as a separate feature and the value in the dictionary corresponds to the value of the feature. Dictionaries are ideal for representing sparse data.
Columns of type list are not supported. Convert such feature columns to type array if all entries in the list are of numeric types. If the lists contain data of mixed types, separate them out into different columns.
- validation_set : SFrame, optional
A dataset for monitoring the model’s generalization performance. For each row of the progress table, the chosen metrics are computed for both the provided training dataset and the validation_set. The format of this SFrame must be the same as the training set. By default this argument is set to ‘auto’ and a validation set is automatically sampled and used for progress printing. If validation_set is set to None, then no additional metrics are computed. The default value is ‘auto’.
- verbose : boolean, optional
If True, print progress information during training.
- out : A trained classifier model.
# Setup the data >>> import turicreate as tc >>> data = tc.SFrame('https://static.turi.com/datasets/regression/houses.csv') >>> data['is_expensive'] = data['price'] > 30000 # Selects the best model based on your data. >>> model = tc.classifier.create(data, target='is_expensive', ... features=['bath', 'bedroom', 'size']) # Make predictions and evaluate results. >>> predictions = model.classify(data) >>> results = model.evaluate(data)