turicreate.random_forest_regression.RandomForestRegression.predict¶

RandomForestRegression.predict(dataset, missing_value_action='auto')¶

Predict the target column of the given dataset.

The target column is provided during create(). If the target column is in the dataset it will be ignored.

Parameters:

dataset : SFrame

A dataset that has the same columns that were used during training. If the target column exists in dataset it will be ignored while making predictions.

missing_value_action : str, optional

Action to perform when missing values are encountered. Can be one of:

‘auto’: By default the model will treat missing value as is.
‘impute’: Proceed with evaluation by filling in the missing values with the mean of the training data. Missing values are also imputed if an entire column of data is missing during evaluation.
‘error’: Do not proceed with evaluation and terminate with an error message.

Returns:

out : SArray: Predicted target value for each example (i.e. row) in the dataset.