evalml.pipelines.RFRegressionPipeline

Inheritance diagram of RFRegressionPipeline
class evalml.pipelines.RFRegressionPipeline(parameters, random_state=0)[source]

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 + RF Regressor Select From Model'
component_graph = ['One Hot Encoder', 'Simple Imputer', 'RF Regressor Select From Model', 'Random Forest Regressor']
problem_type = 'regression'
model_family = 'random_forest'
hyperparameters = {'impute_strategy': ['mean', 'median', 'most_frequent'], 'max_depth': Integer(low=1, high=32, prior='uniform', transform='identity'), 'n_estimators': Integer(low=10, high=1000, prior='uniform', transform='identity'), 'percent_features': Real(low=0.01, high=1, prior='uniform', transform='identity'), 'threshold': ['mean', -inf]}
custom_hyperparameters = None

Instance attributes

feature_importances

Return feature importances.

parameters

Returns parameter dictionary for this pipeline

Methods:

__init__

Machine learning pipeline made out of transformers and a estimator.

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 importances

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