# turicreate.random_forest_classifier.create¶

turicreate.random_forest_classifier.create(dataset, target, features=None, max_iterations=10, validation_set='auto', verbose=True, class_weights=None, random_seed=None, metric='auto', **kwargs)

Create a (binary or multi-class) classifier model of type RandomForestClassifier using an ensemble of decision trees trained on subsets of the data.

Parameters: dataset : SFrame A training dataset containing feature columns and a target column. target : str 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 alphabetical order of the variable values. For example, a target variable with ‘cat’, ‘dog’, and ‘foosa’ as possible values is mapped to 0, 1, and, 2 respectively. features : list[str], optional A list of columns names of features used for training the model. Defaults to None, which uses all columns in the SFrame dataset excepting the target column.. max_iterations : int, optional The maximum number of iterations to perform. For multi-class classification with K classes, each iteration will create K-1 trees. max_depth : float, optional Maximum depth of a tree. class_weights : {dict, auto}, optional Weights the examples in the training data according to the given class weights. If set to None, all classes are supposed to have weight one. The auto mode set the class weight to be inversely proportional to number of examples in the training data with the given class. min_loss_reduction : float, optional (non-negative) Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger it is, the more conservative the algorithm will be. Must be non-negative. 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, optional Subsample the ratio of the training set in each iteration of tree construction. This is called the bagging trick and can usually help prevent overfitting. Setting this to a value of 0.5 results in the model randomly sampling half of the examples (rows) to grow each tree. column_subsample : float, optional Subsample ratio of the columns in each iteration of tree construction. Like row_subsample, this can also help prevent model overfitting. Setting this to a value of 0.5 results in the model randomly sampling half of the columns to grow each tree. 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. This is computed once per full iteration. Large differences in model accuracy between the training data and validation data is indicative of overfitting. The default value is ‘auto’. verbose : boolean, optional Print progress information during training (if set to true). 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: {‘accuracy’, ‘auc’, ‘log_loss’} kwargs : dict, optional Additional arguments for training the model. 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. out : RandomForestClassifier A trained random forest model for classification tasks.

References

Examples

>>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'