turicreate.boosted_trees_regression.create

turicreate.boosted_trees_regression.create(dataset, target, features=None, max_iterations=10, validation_set='auto', max_depth=6, step_size=0.3, min_loss_reduction=0.0, min_child_weight=0.1, row_subsample=1.0, column_subsample=1.0, verbose=True, random_seed=None, metric='auto', **kwargs)

Create a BoostedTreesRegression to predict a scalar target variable using one or more features. In addition to standard numeric and categorical types, features can also be extracted automatically from list- or dictionary-type SFrame columns.

Parameters:
dataset : SFrame

A training dataset containing feature columns and a target column. Only numerical typed (int, float) target column is allowed.

target : str

The name of the column in dataset that is the prediction target. This column must have a numeric type.

features : list[str], optional

A list of columns names of features used for training the model. Defaults to None, using all columns.

max_iterations : int, optional

The number of iterations for boosting. It is also the number of trees in the model.

validation_set : SFrame, optional

The validation set that is used to watch the validation result as boosting progress.

max_depth : float, optional

Maximum depth of a tree. Must be at least 1.

step_size : float, [0,1], optional

Step size (shrinkage) used in update to prevents overfitting. It shrinks the prediction of each weak learner to make the boosting process more conservative. The smaller, the more conservative the algorithm will be. Smaller step_size is usually used together with larger max_iterations.

min_loss_reduction : float, optional (non-negative)

Minimum loss reduction required to make a further partition/split a node during the tree learning phase. Larger (more positive) values can help prevent overfitting by avoiding splits that do not sufficiently reduce the loss function.

min_child_weight : float, optional (non-negative)

Controls the minimum weight of each leaf node. Larger values result in more conservative tree learning and help prevent overfitting. Formally, this is minimum sum of instance weights (hessians) in each node. If the tree learning algorithm results in a leaf node with the sum of instance weights less than min_child_weight, tree building will terminate.

row_subsample : float, [0,1], optional

Subsample the ratio of the training set in each iteration of tree construction. This is called the bagging trick and usually can help prevent overfitting. Setting it to 0.5 means that model randomly collected half of the examples (rows) to grow each tree.

column_subsample : float, [0,1], optional

Subsample ratio of the columns in each iteration of tree construction. Like row_subsample, this also usually can help prevent overfitting. Setting it to 0.5 means that model randomly collected half of the columns to grow each tree.

verbose : boolean, optional

If True, print progress information during training.

random_seed: int, optional

Seeds random operations such as column and row subsampling, such that results are reproducible.

metric : str or list[str], optional

Performance metric(s) that are tracked during training. When specified, the progress table will display the tracked metric(s) on training and validation set. Supported metrics are: {‘rmse’, ‘max_error’}

kwargs : dict, optional

Additional arguments for training the model.

  • early_stopping_rounds : int, default None
    If the validation metric does not improve after <early_stopping_rounds>, stop training and return the best model. If multiple metrics are being tracked, the last one is used.
  • model_checkpoint_path : str, default None
    If specified, checkpoint the model training to the given path every n iterations, where n is specified by model_checkpoint_interval. For instance, if model_checkpoint_interval is 5, and model_checkpoint_path is set to /tmp/model_tmp, the checkpoints will be saved into /tmp/model_tmp/model_checkpoint_5, /tmp/model_tmp/model_checkpoint_10, … etc. Training can be resumed by setting resume_from_checkpoint to one of these checkpoints.
  • model_checkpoint_interval : int, default 5
    If model_check_point_path is specified, save the model to the given path every n iterations.
  • resume_from_checkpoint : str, default None
    Continues training from a model checkpoint. The model must take exact the same training data as the checkpointed model.
Returns:
out : BoostedTreesRegression

A trained gradient boosted trees model

See also

BoostedTreesRegression, turicreate.linear_regression.LinearRegression, turicreate.regression.create

References

Examples

Setup the data:

>>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'
>>> data = turicreate.SFrame.read_csv(url)
>>> data['label'] = data['label'] == 'p'

Split the data into training and test data:

>>> train, test = data.random_split(0.8)

Create the model:

>>> model = turicreate.boosted_trees_regression.create(train, target='label')

Make predictions and evaluate the model:

>>> predictions = model.predict(test)
>>> results = model.evaluate(test)