evalml.pipelines.RFClassificationPipeline

class evalml.pipelines.RFClassificationPipeline(parameters, objective, random_state=0)[source]

Random Forest Pipeline for both binary and multiclass classification

name = 'Random Forest Classification Pipeline'
summary = 'Random Forest Classifier w/ One Hot Encoder + Simple Imputer + RF Classifier Select From Model'
component_graph = ['One Hot Encoder', 'Simple Imputer', 'RF Classifier Select From Model', <evalml.pipelines.components.estimators.classifiers.rf_classifier.RandomForestClassifier object>]
supported_problem_types = ['binary', 'multiclass']
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

feature_importance_graph

Generate a bar graph of the pipeline’s feature importances

fit

Build a model

get_component

Returns component by name

graph

Generate an image representing the pipeline graph

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 current and additional objectives