evalml.pipelines.RFBinaryClassificationPipeline

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

Random Forest Pipeline for binary classification.

name = 'Random Forest Binary Classification Pipeline'

custom_name = 'Random Forest Binary Classification Pipeline'

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

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

problem_type = 'binary'

model_family = 'random_forest'

hyperparameters = {'One Hot Encoder': {}, 'Random Forest Classifier': {'max_depth': Integer(low=1, high=10, 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 Classifier': {'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
threshold

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.
predict_proba	Make probability estimates for labels.
save	Saves pipeline at file path
score	Evaluate model performance on objectives