evalml.pipelines.RFRegressionPipeline

class evalml.pipelines.RFRegressionPipeline(parameters, random_state=0)

Random Forest Pipeline for regression problems.

name = 'Random Forest Regression Pipeline'

custom_name = 'Random Forest Regression Pipeline'

summary = 'Random Forest Regressor w/ One Hot Encoder + Simple Imputer'

component_graph = ['One Hot Encoder', 'Simple Imputer', 'Random Forest Regressor']

problem_type = 'regression'

model_family = 'random_forest'

hyperparameters = {'One Hot Encoder': {}, 'Random Forest Regressor': {'max_depth': Integer(low=1, high=32, prior='uniform', transform='identity'), 'n_estimators': Integer(low=10, high=1000, prior='uniform', transform='identity')}, 'Simple Imputer': {'impute_strategy': ['mean', 'median', 'most_frequent']}}

custom_hyperparameters = None

default_parameters = {'One Hot Encoder': {'categories': None, 'drop': None, 'handle_missing': 'error', 'handle_unknown': 'ignore', 'top_n': 10}, 'Random Forest Regressor': {'max_depth': 6, 'n_estimators': 100, 'n_jobs': -1}, 'Simple Imputer': {'fill_value': None, 'impute_strategy': 'most_frequent'}}

Instance attributes

feature_importance	Return importance associated with each feature.
parameters	Returns parameter dictionary for this pipeline

Methods:

__init__	Machine learning pipeline made out of transformers and a estimator.
clone	Constructs a new pipeline with the same parameters and components.
describe	Outputs pipeline details including component parameters
fit	Build a model
get_component	Returns component by name
graph	Generate an image representing the pipeline graph
graph_feature_importance	Generate a bar graph of the pipeline’s feature importance
load	Loads pipeline at file path
predict	Make predictions using selected features.
save	Saves pipeline at file path
score	Evaluate model performance on current and additional objectives