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 dictionarytype 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 (nonnegative)
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 (nonnegative)
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 settingresume_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
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)